The PASCAL Visual Object Classes Challenge 2009 (VOC2009) Development Kit

Mark Everingham - John Winn


Contents

1 Challenge

The goal of this challenge is to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). There are twenty object classes: There are three main tasks: In addition, there is a single ``taster'' task:

2 Data

The VOC2009 database contains a total of 14,743 annotated images. The data is released in two phases: (i) training and validation data with annotation is released with this development kit; (ii) test data without annotation is released at a later date.

2.1 Classification/Detection Image Sets

For the classification and detection tasks there are four sets of images provided:
train:
Training data
val:
Validation data (suggested). The validation data may be used as additional training data (see below).
trainval:
The union of train and val.
test:
Test data. The test set is not provided in the development kit. It will be released in good time before the deadline for submission of results.
Table 1 summarizes the number of objects and images (containing at least one object of a given class) for each class and image set. The data has been split into 50% for training/validation and 50% for testing. The distributions of images and objects by class are approximately equal across the training/validation and test sets. To increase the amount of data, the dataset includes images from the 2008 dataset, indicated by the `2008' prefix. The assignment of images to training/test sets follows the 2008 assignments i.e. the 2008 training/test sets are a subset of the corresponding 2009 sets. Note that no annotation for the 2008 test set has been released.
Table 1: Statistics of the main image sets. Object statistics list only the `non-difficult' objects used in the evaluation.

train val trainval test
  img obj img obj img obj img obj

Aeroplane		 201 267 206 266 407 533 - -
Bicycle 167 232 181 236 348 468 - -
Bird 262 381 243 379 505 760 - -
Boat 170 270 155 267 325 537 - -
Bottle 220 394 200 393 420 787 - -
Bus 132 179 126 186 258 365 - -
Car 372 664 358 653 730 1317 - -
Cat 266 308 277 314 543 622 - -
Chair 338 716 330 713 668 1429 - -
Cow 86 164 86 172 172 336 - -
Diningtable 140 153 131 153 271 306 - -
Dog 316 391 333 392 649 783 - -
Horse 161 237 167 245 328 482 - -
Motorbike 171 235 167 234 338 469 - -
Person 1333 2819 1446 2996 2779 5815 - -
Pottedplant 166 311 166 316 332 627 - -
Sheep 67 163 64 175 131 338 - -
Sofa 155 172 153 175 308 347 - -
Train 164 190 160 191 324 381 - -
Tvmonitor 180 259 173 257 353 516 - -
Total 3473 8505 3581 8713 7054 17218 - -

2.2 Segmentation Image Sets

For the segmentation task, corresponding image sets are provided as in the classification/detection tasks. To increase the amount of data, the training and validation image sets include images from the 2007/2008 segmentation tasters, indicated by the `2007/2008' prefix. The test set contains only 2008/2009 images (i.e. those for which no annotation has been released), and is a subset of the test set for the main tasks for which pixel-wise segmentations have been prepared. Table 2 summarizes the number of objects and images (containing at least one object of a given class) for each class and image set, for the combined 2007-9 data. In addition to the segmented images for training and validation, participants are free to use the un-segmented training/validation images supplied for the main classification/detection tasks, and any annotation provided for the main challenge e.g. bounding boxes.
Table 2: Statistics of the segmentation image sets.

train val trainval test
  img obj img obj img obj img obj

Aeroplane		 47 53 40 48 87 101 - -
Bicycle 39 51 38 50 77 101 - -
Bird 55 74 52 64 107 138 - -
Boat 48 75 39 48 87 123 - -
Bottle 42 75 44 61 86 136 - -
Bus 38 48 39 59 77 107 - -
Car 63 94 51 96 114 190 - -
Cat 45 58 53 58 98 116 - -
Chair 69 152 55 108 124 260 - -
Cow 30 67 36 62 66 129 - -
Diningtable 48 49 40 43 88 92 - -
Dog 43 52 58 71 101 123 - -
Horse 42 57 50 60 92 117 - -
Motorbike 47 51 36 49 83 100 - -
Person 207 352 210 368 417 720 - -
Pottedplant 43 66 45 97 88 163 - -
Sheep 27 64 34 88 61 152 - -
Sofa 44 52 53 65 97 117 - -
Train 40 47 46 51 86 98 - -
Tvmonitor 51 64 48 64 99 128 - -
Total 749 1601 750 1610 1499 3211 - -

2.3 Person Layout Taster Image Sets

For the person layout taster task, corresponding image sets are provided as in the classification/detection tasks. A person is indicated by a bounding box, and each person has been annotated with part layout (head, hands, feet). As in the segmentation task, the training and validation image sets include images from the 2007/2008 person layout tasters, indicated by the `2007/2008' prefix. The test set contains only 2008/2009 images (i.e. those for which no annotation has been released), and is disjoint from the test set for the main tasks. Table 3 summarizes the number of `person' objects annotated with layout for each image set.
Table 3: Statistics of the person layout taster image sets. Object statistics list only the `person' objects for which layout information (parts) is present.

train val trainval test
  img obj img obj img obj img obj

Person		 178 256 139 219 317 475 - -


2.4 Ground Truth Annotation

Objects of the twenty classes listed above are annotated in the ground truth. For each object, the following annotation is present: In preparing the ground truth, annotators were given a detailed list of guidelines on how to complete the annotation. These are available on the main challenge web-site [1].

2.5 Segmentation Ground Truth

Figure 1: Example of segmentation ground truth. a. Training image b. Class segmentation showing background, car, horse and person labels. The cream-colored `void' label is also used in border regions and to mask difficult objects. c. Object segmentation where individual object instances are separately labelled.
\includegraphics[height=5cm]{inpseg.eps} \includegraphics[height=5cm]{clsseg.eps} \includegraphics[height=5cm]{objseg.eps}
a. b. c.
For the segmentation image sets, each image has two corresponding types of ground truth segmentation provided: Figure 1 gives an example of these two types of segmentation for one of the training set images. The ground truth segmentations are provided to a high degree of accuracy, but are not pixel accurate, as this would have greatly extended the time required to gather these segmentations. Instead, they were labelled so that a bordering region with a width of five pixels may contain either object or background. Bordering regions are marked with a `void' label (index 255), indicating that the contained pixels can be any class including background. The void label is also used to mask out ambiguous, difficult or heavily occluded objects and also to label regions of the image containing objects too small to be marked, such as crowds of people. All void pixels are ignored when computing segmentation accuracies and should be treated as unlabelled pixels during training. In addition to the ground truth segmentations given, participants are free to use any of the ground truth annotation for the classification/detection tasks e.g. bounding boxes.

2.6 Person Layout Taster Ground Truth

For the person layout taster task, `person' objects are additionally annotated with three `parts': For each annotated person, the presence or absence of each part is listed, and for each part present, the bounding box is specified. The test images for the person layout taster are disjoint from the main image sets. There are no `difficult' objects.

3 Classification Task

3.1 Task

For each of the twenty object classes predict the presence/absence of at least one object of that class in a test image. The output from your system should be a real-valued confidence of the object's presence so that a precision/recall curve can be drawn. Participants may choose to tackle all, or any subset of object classes, for example ``cars only'' or ``motorbikes and cars''.

3.2 Competitions

Two competitions are defined according to the choice of training data: (i) taken from the VOC trainval data provided, or (ii) from any source excluding the VOC test data provided:
No. Task Training data Test data
1 Classification trainval test
2 Classification any but VOC test test
In competition 1, any annotation provided in the VOC train and val sets may be used for training, for example bounding boxes or particular views e.g. `frontal' or `left'. Participants are not permitted to perform additional manual annotation of either training or test data. In competition 2, any source of training data may be used except the provided test images. Researchers who have pre-built systems trained on other data are particularly encouraged to participate. The test data includes images from ``flickr'' (www.flickr.com); this source of images may not be used for training. Participants who have acquired images from flickr for training must submit them to the organizers to check for overlap with the test set.

3.3 Submission of Results

A separate text file of results should be generated for each competition (1 or 2) and each class e.g. `car'. Each line should contain a single identifier and the confidence output by the classifier, separated by a space, for example: 
comp1_cls_test_car.txt:
    ...
    2009_000001 0.056313
    2009_000002 0.127031
    2009_000009 0.287153
    ...
Greater confidence values signify greater confidence that the image contains an object of the class of interest. The example classifier implementation (section 7.2.1) includes code for generating a results file in the required format.


3.4 Evaluation

The classification task will be judged by the precision/recall curve. The principal quantitative measure used will be the average precision (AP). Example code for computing the precision/recall and AP measure is provided in the development kit. Images which contain only objects marked as `difficult' (section 2.4) are currently ignored by the evaluation. The final evaluation may include separate results including such ``difficult'' images, depending on the submitted results. Participants are expected to submit a single set of results per method employed. Participants who have investigated several algorithms may submit one result per method. Changes in algorithm parameters do not constitute a different method - all parameter tuning must be conducted using the training and validation data alone.

4 Detection Task

4.1 Task

For each of the twenty classes predict the bounding boxes of each object of that class in a test image (if any). Each bounding box should be output with an associated real-valued confidence of the detection so that a precision/recall curve can be drawn. Participants may choose to tackle all, or any subset of object classes, for example ``cars only'' or ``motorbikes and cars''.

4.2 Competitions

Two competitions are defined according to the choice of training data: (i) taken from the VOC trainval data provided, or (ii) from any source excluding the VOC test data provided:
No. Task Training data Test data
3 Detection trainval test
4 Detection any but VOC test test
In competition 3, any annotation provided in the VOC train and val sets may be used for training, for example bounding boxes or particular views e.g. `frontal' or `left'. Participants are not permitted to perform additional manual annotation of either training or test data. In competition 4, any source of training data may be used except the provided test images. Researchers who have pre-built systems trained on other data are particularly encouraged to participate. The test data includes images from ``flickr'' (www.flickr.com); this source of images may not be used for training. Participants who have acquired images from flickr for training must submit them to the organizers to check for overlap with the test set.

4.3 Submission of Results

A separate text file of results should be generated for each competition (3 or 4) and each class e.g. `car'. Each line should be a detection output by the detector in the following format: 
    <image identifier> <confidence> <left> <top> <right> <bottom>
where (left,top)-(right,bottom) defines the bounding box of the detected object. The top-left pixel in the image has coordinates $(1,1)$. Greater confidence values signify greater confidence that the detection is correct. An example file excerpt is shown below. Note that for the image 2009_000032, multiple objects are detected: 
comp3_det_test_car.txt:
    ...
    2009_000026 0.949297 172.000000 233.000000 191.000000 248.000000
    2009_000032 0.013737 1.000000 147.000000 114.000000 242.000000
    2009_000032 0.013737 1.000000 134.000000 94.000000 168.000000
    2009_000035 0.063948 455.000000 229.000000 491.000000 243.000000
    ...
The example detector implementation (section 7.2.2) includes code for generating a results file in the required format.

4.4 Evaluation

The detection task will be judged by the precision/recall curve. The principal quantitative measure used will be the average precision (AP). Example code for computing the precision/recall and AP measure is provided in the development kit. Detections are considered true or false positives based on the area of overlap with ground truth bounding boxes. To be considered a correct detection, the area of overlap $a_o$ between the predicted bounding box $B_p$ and ground truth bounding box $B_{gt}$ must exceed $50\%$ by the formula:
\begin{displaymath} a_o = \frac{area(B_p \cap B_{gt})}{area(B_p \cup B_{gt})} \end{displaymath} (1)

Example code for computing this overlap measure is provided in the development kit. Multiple detections of the same object in an image are considered false detections e.g. 5 detections of a single object is counted as 1 correct detection and 4 false detections - it is the responsibility of the participant's system to filter multiple detections from its output. Objects marked as `difficult' (section 2.4) are currently ignored by the evaluation. The final evaluation may include separate results including such ``difficult'' images, depending on the submitted results. Participants are expected to submit a single set of results per method employed. Participants who have investigated several algorithms may submit one result per method. Changes in algorithm parameters do not constitute a different method - all parameter tuning must be conducted using the training and validation data alone.

5 Segmentation Task

5.1 Task

For each test image pixel, predict the class of the object containing that pixel or 'background' if the pixel does not belong to one of the twenty specified classes. The output from your system should be an indexed image with each pixel index indicating the number of the inferred class (1-20) or zero, indicating background.

5.2 Competitions

Two competitions are defined according to the choice of training data: (i) taken from the VOC trainval data provided, or (ii) from any source excluding the VOC test data provided:
No. Task Training data Test data
5 Segmentation trainval test
6 Segmentation any but VOC test test
For competition 5, any annotation provided in the VOC train and val sets may be used for training, for example segmentation, bounding boxes or particular views e.g. `frontal' or `left'. However, if training uses annotation of any images other than the segmented training images, this must be reported as part of the submission (see below) since this allows a considerably larger training set. Participants are not permitted to perform additional manual annotation of either training or test data. For competition 6, any source of training data may be used except the provided test images.

5.3 Submission of Results

Submission of results should be as collections of PNG format indexed image files, one per test image, with pixel indices from 0 to 20. The PNG color map should be the same as the color map used in the provided training and validation annotation (MATLAB users can use VOClabelcolormap - see section 8.5.3). The example segmenter implementation (section 7.2.3) includes code for generating results in the required format. Participants may choose to include segmentations for only a subset of the 20 classes in which case they will be evaluated on only the included classes. For competition 5, along with the submitted image files, participants must also state whether their method used segmentation training data only or both segmentation and bounding box training data. This information will be used when analysing and presenting the competition results.

5.4 Evaluation

Each segmentation competition will be judged by average segmentation accuracy across the twenty classes and the background class. The segmentation accuracy for a class will be assessed using the intersection/union metric, defined as the number of correctly labelled pixels of that class, divided by the number of pixels labelled with that class in either the ground truth labelling or the inferred labelling. Equivalently, the accuracy is given by the equation,
$\displaystyle \mathrm{segmentation accuracy} = \frac{\mathrm{true positives}}{\mathrm{true positives} + \mathrm{false positives} + \mathrm{false negatives}}$      

Code is provided to compute segmentation accuracies for each class, and the overall average accuracy (see section 8.5.2). Participants are expected to submit a single set of results per method employed. Participants who have investigated several algorithms may submit one result per method. Changes in algorithm parameters do not constitute a different method - all parameter tuning must be conducted using the training and validation data alone.

6 Person Layout Taster

6.1 Task

For each `person' object in a test image (their bounding box is provided) predict the presence/absence of parts (head/hands/feet), and the bounding boxes of those parts. Each prediction for a person layout should be output with an associated real-valued confidence of the layout so that a precision/recall curve can be drawn. Multiple estimates of layout may be output for each person, but estimates other than the first correct are treated as false positives as in the detection task. As noted, the bounding box of the person is provided. To be considered a correct estimate of the layout, two conditions must be satisfied: i) correct prediction of parts present/absent; ii) correct prediction of bounding boxes for all parts.

6.2 Competitions

Two competitions are defined according to the choice of training data: (i) taken from the VOC trainval data provided, or (ii) from any source excluding the VOC test data provided:
No. Task Training data Test data
7 Layout trainval test
8 Layout any but VOC test test
In competition 7, any annotation provided in the VOC train and val sets may be used for training, for example bounding boxes or particular views e.g. `frontal' or `left'. Participants are not permitted to perform additional manual annotation of either training or test data. In competition 8, any source of training data may be used except the provided test images. Researchers who have pre-built systems trained on other data are particularly encouraged to participate. The test data includes images from ``flickr'' (www.flickr.com); this source of images may not be used for training. Participants who have acquired images from flickr for training must submit them to the organizers to check for overlap with the test set.

6.3 Submission of Results

To support the hierarchical (person+parts) nature of this task, an XML format has been adopted for submission of results. A separate XML file of results should be generated for each competition (6 or 7). The overall format should follow: 
<results>
    <layout>
        ... layout estimate 1 ...
    </layout>
    <layout>
        ... layout estimate 2 ...
    </layout>
</results>
Each detection is represented by a <layout> element. The order of detections is not important. An example detection is shown here: 
    <layout>
        <image>2009_000183</image>
        <object>1</object>
        <confidence>-1189</confidence>
        <part>
            <class>head</class>
            <bndbox>
                <xmin>191</xmin>
                <ymin>25</ymin>
                <xmax>323</xmax>
                <ymax>209</ymax>
            </bndbox>
        </part>
        <part>
            <class>hand</class>
            <bndbox>
                <xmin>393</xmin>
                <ymin>206</ymin>
                <xmax>488</xmax>
                <ymax>300</ymax>
            </bndbox>
        </part>
        <part>
            <class>hand</class>
            <bndbox>
                <xmin>1</xmin>
                <ymin>148</ymin>
                <xmax>132</xmax>
                <ymax>329</ymax>
            </bndbox>
        </part>
    </layout>
The <image> element specifies the image identifier. The <object> specifies the index of the object to which the layout relates (the first object in the image has index 1) and should match that provided in the image set files (section 8.1.4). The <confidence> element specifies the confidence of the layout estimate, used to generate a precision/recall curve as in the detection task. Each <part> element specifies the detection of a particular part of the person i.e. head/hand/foot. If the part is predicted to be absent/invisible, the corresponding element should be omitted. For each part, the <class> element specifies the type of part: head, hand or foot. The <bndbox> element specifies the predicted bounding box for that part; bounding boxes are specified in image co-ordinates and need not be contained in the provided person bounding box. To ease creation of the required XML results file for MATLAB users, a function is included in the development kit to convert MATLAB structures to XML. See the VOCwritexml function (section 8.6.1). The example person layout implementation (section 7.2.4) includes code for generating a results file in the required format.

6.4 Evaluation

The person layout task will be judged by the precision/recall curve. The principal quantitative measure used will be the average precision (AP). Example code for computing the precision/recall and AP measure is provided in the development kit. To be considered a true positive, each layout estimate must satisfy two criteria: The overlap between bounding boxes is computed as in the detection task. Note that in the case of multiple parts of the same type e.g. two hands, it is not necessary to predict which part is which.

7 Development Kit

The development kit is packaged in a single gzipped tar file containing MATLAB code and (this) documentation. The images, annotation, and lists specifying training/validation sets for the challenge are provided in a separate archive which can be obtained via the VOC web pages [1].

7.1 Installation and Configuration

The simplest installation is achieved by placing the development kit and challenge databases in a single location. After untarring the development kit, download the challenge image database and untar into the same directory, resulting in the following directory structure: 
    VOCdevkit/                           % development kit
    VOCdevkit/VOCcode/                   % VOC utility code
    VOCdevkit/results/VOC2009/           % your results on VOC2009
    VOCdevkit/local/                     % example code temp dirs
    VOCdevkit/VOC2009/ImageSets          % image sets
    VOCdevkit/VOC2009/Annotations        % annotation files
    VOCdevkit/VOC2009/JPEGImages         % images
    VOCdevkit/VOC2009/SegmentationObject % segmentations by object
    VOCdevkit/VOC2009/SegmentationClass  % segmentations by class
If you set the current directory in MATLAB to the VOCdevkit directory you should be able to run the example functions: If desired, you can store the code, images/annotation, and results in separate directories, for example you might want to store the image data in a common group location. To specify the locations of the image/annotation, results, and working directories, edit the VOCinit.m file, e.g. 
% change this path to point to your copy of the PASCAL VOC data
VOCopts.datadir='/homes/group/VOCdata/';

% change this path to a writable directory for your results
VOCopts.resdir='/homes/me/VOCresults/';

% change this path to a writable local directory for the example code
VOCopts.localdir='/tmp/';
Note that in developing your own code you need to include the VOCdevkit/VOCcode directory in your MATLAB path, e.g. 
>> addpath /homes/me/code/VOCdevkit/VOCcode

7.2 Example Code

Example implementations are provided for all tasks. The aim of these (minimal) implementations is solely to demonstrate use of the code in the development kit.


7.2.1 Example Classifier Implementation

The file example_classifier.m contains a complete implementation of the classification task. For each VOC object class a simple classifier is trained on the train set; the classifier is then applied to the val set and the output saved to a results file in the format required by the challenge; a precision/recall curve is plotted and the `average precision' (AP) measure displayed.


7.2.2 Example Detector Implementation

The file example_detector.m contains a complete implementation of the detection task. For each VOC object class a simple (and not very successful!) detector is trained on the train set; the detector is then applied to the val set and the output saved to a results file in the format required by the challenge; a precision/recall curve is plotted and the `average precision' (AP) measure displayed.


7.2.3 Example Segmenter Implementation

An example segmenter is provided which converts detection results into segmentation results, using create_segmentations_from_detections (described below). For example: 
>> example_detector;
>> example_segmenter;
This runs the example detector, converts the detections into segmentations and displays a table of per-class segmentation accuracies, along with an overall average accuracy.


7.2.4 Example Layout Implementation

The file example_layout.m contains a complete implementation of the person layout task. For each specified person a simple (and not very successful!) layout predictor is trained on the train set; the layout predictor is then applied to the val set and the output saved to a results file in the format required by the challenge; a precision/recall curve is plotted and the `average precision' (AP) measure displayed.

7.3 Non-MATLAB Users

For non-MATLAB users, the file formats used for the VOC2009 data should be straightforward to use in other environments. Image sets (see below) are vanilla text files. Annotation files are XML format and should be readable by any standard XML parser. Images are stored in JPEG format, and segmentation ground truth in PNG format.

8 Using the Development Kit

The development kit provides functions for loading annotation data. Example code for computing precision/recall curves and segmentation accuracy, and for viewing annotation is also provided.

8.1 Image Sets

8.1.1 Classification/Detection Task Image Sets

The VOC2009/ImageSets/Main/ directory contains text files specifying lists of images for the main classification/detection tasks. The files train.txt, val.txt, trainval.txt and test.txt list the image identifiers for the corresponding image sets (training, validation, training+validation and testing). Each line of the file contains a single image identifier. The following MATLAB code reads the image list into a cell array of strings: 
imgset='train';
ids=textread(sprintf(VOCopts.imgsetpath,imgset),'%s');
For a given image identifier ids{i}, the corresponding image and annotation file paths can be produced thus: 
imgpath=sprintf(VOCopts.imgpath,ids{i});
annopath=sprintf(VOCopts.annopath,ids{i});
Note that the image sets used are the same for all classes. For each competition, participants are expected to provide output for all images in the test set.

8.1.2 Classification Task Image Sets

To simplify matters for participants tackling only the classification task, class-specific image sets with per-image ground truth are also provided. The file VOC2009/ImageSets/Main/<class>_<imgset>.txt contains image identifiers and ground truth for a particular class and image set, for example the file car_train.txt applies to the `car' class and train image set. Each line of the file contains a single image identifier and ground truth label, separated by a space, for example: 
    ...
    2009_000040 -1
    2009_000042 -1
    2009_000052  1
    ...
The following MATLAB code reads the image list into a cell array of strings and the ground truth label into a corresponding vector: 
imgset='train';
cls='car';
[ids,gt]=textread(sprintf(VOCopts.clsimgsetpath, ...
                    cls,imgset),'%s %d');
There are three ground truth labels:
-1:
Negative: The image contains no objects of the class of interest. A classifier should give a `negative' output.
1:
Positive: The image contains at least one object of the class of interest. A classifier should give a `positive' output.
0:
``Difficult'': The image contains only objects of the class of interest marked as `difficult'.
The ``difficult'' label indicates that all objects of the class of interest have been annotated as ``difficult'', for example an object which is clearly visible but difficult to recognize without substantial use of context. Currently the evaluation ignores such images, contributing nothing to the precision/recall curve or AP measure. The final evaluation may include separate results including such ``difficult'' images, depending on the submitted results. Participants are free to omit these images from training or include as either positive or negative examples.

8.1.3 Segmentation Image Sets

The VOC2009/ImageSets/Segmentation/ directory contains text files specifying lists of images for the segmentation task. The files train.txt, val.txt, trainval.txt and test.txt list the image identifiers for the corresponding image sets (training, validation, training+validation and testing). Each line of the file contains a single image identifier. The following MATLAB code reads the image list into a cell array of strings: 
imgset='train';
ids=textread(sprintf(VOCopts.seg.imgsetpath,imgset),'%s');
For a given image identifier ids{i}, file paths for the corresponding image, annotation, segmentation by object instance and segmentation by class can be produced thus: 
imgpath=sprintf(VOCopts.imgpath,ids{i});
annopath=sprintf(VOCopts.annopath,ids{i});
clssegpath=sprintf(VOCopts.seg.clsimgpath,ids{i});
objsegpath=sprintf(VOCopts.seg.instimgpath,ids{i});
Participants are expected to provide output for all images in the test set.


8.1.4 Person Layout Taster Image Sets

The VOC2009/ImageSets/Layout/ directory contains text files specifying lists of image for the person layout taster task. The files train.txt, val.txt, trainval.txt and test.txt list the image identifiers for the corresponding image sets (training, validation, training+validation and testing). Each line of the file contains a single image identifier, and a single object index. Together these specify a `person' object for which layout is provided or to be estimated, for example: 
    ...
    2009_000595  1
    2009_000595  2
    2009_000606  1
    ...
The following MATLAB code reads the image list into a cell array of strings and the object indices into a corresponding vector: 
imgset='train';
[imgids,objids]=textread(sprintf(VOCopts.layout.imgsetpath, ...
                            VOCopts.trainset),'%s %d');
The annotation for the object (bounding box only in the test data) can then be obtained using the image identifier and object index: 
    rec=PASreadrecord(sprintf(VOCopts.annopath,imgids{i}));
    obj=rec.objects(objids{i});

8.2 Development Kit Functions

8.2.1 VOCinit

The VOCinit script initializes a single structure VOCopts which contains options for the PASCAL functions including directories containing the VOC data and options for the evaluation functions (not to be modified). The field classes lists the object classes for the challenge in a cell array: 
VOCopts.classes={'aeroplane','bicycle','bird','boat',...
                 'bottle','bus','car','cat',...
                 'chair','cow','diningtable','dog',...
                 'horse','motorbike','person','pottedplant',...
                 'sheep','sofa','train','tvmonitor'};
The field trainset specifies the image set used by the example evaluation functions for training: 
VOCopts.trainset='train'; % use train for development
Note that participants are free to use both training and validation data in any manner they choose for the final challenge. The field testset specifies the image set used by the example evaluation functions for testing: 
VOCopts.testset='val'; % use validation data for development
Other fields provide, for convenience, paths for the image and annotation data and results files. The use of these paths is illustrated in the example implementations.

8.2.2 PASreadrecord(filename)

The PASreadrecord function reads the annotation data for a particular image from the annotation file specified by filename, for example: 
>> rec=PASreadrecord(sprintf(VOCopts.annopath,'2009_000067'))

rec =

       folder: 'VOC2009'
     filename: '2009_000067.jpg'
       source: [1x1 struct]
         size: [1x1 struct]
    segmented: 0
      imgname: 'VOC2009/JPEGImages/2009_000067.jpg'
      imgsize: [500 334 3]
     database: 'The VOC2009 Database'
      objects: [1x6 struct]
The imgname field specifies the path (relative to the main VOC data path) of the corresponding image. The imgsize field specifies the image dimensions as (width,height,depth). The database field specifies the data source (VOC2009). The segmented field specifies if a segmentation is available for this image. The folder and filename fields provide an alternative specification of the image path, and size an alternative specification of the image size: 
>> rec.size

ans =

     width: 500
    height: 334
     depth: 3
The source field contains additional information about the source of the image e.g. web-site and owner. This information is obscured until completion of the challenge. Objects annotated in the image are stored in the struct array objects, for example: 
>> rec.objects(2)

ans =

        class: 'person'
         view: 'Right'
    truncated: 0
     occluded: 0
    difficult: 0
        label: 'PASpersonRight'
     orglabel: 'PASpersonRight'
         bbox: [225 140 270 308]
       bndbox: [1x1 struct]
      polygon: []
         mask: []
     hasparts: 1
         part: [1x4 struct]
The class field contains the object class. The view field contains the view: Frontal, Rear, Left (side view, facing left of image), Right (side view, facing right of image), or an empty string indicating another, or un-annotated view. The truncated field being set to 1 indicates that the object is ``truncated'' in the image. The definition of truncated is that the bounding box of the object specified does not correspond to the full extent of the object e.g. an image of a person from the waist up, or a view of a car extending outside the image. Participants are free to use or ignore this field as they see fit. The occluded field being set to 1 indicates that the object is significantly occluded by another object. Participants are free to use or ignore this field as they see fit. The difficult field being set to 1 indicates that the object has been annotated as ``difficult'', for example an object which is clearly visible but difficult to recognize without substantial use of context. Currently the evaluation ignores such objects, contributing nothing to the precision/recall curve. The final evaluation may include separate results including such ``difficult'' objects, depending on the submitted results. Participants may include or exclude these objects from training as they see fit. The bbox field specifies the bounding box of the object in the image, as [left,top,right,bottom]. The top-left pixel in the image has coordinates $(1,1)$. The bndbox field specifies the bounding box in an alternate form: 
>> rec.objects(2).bndbox

ans =

    xmin: 225
    ymin: 140
    xmax: 270
    ymax: 308
For backward compatibility, the label and orglabel fields specify the PASCAL label for the object, comprised of class, view and truncated/difficult flags. The polygon and mask specify polygon/per-object segmentations, and are not provided for the VOC2009 data. The hasparts field specifies if the object has sub-object ``parts'' annotated. For the VOC2009 data, such annotation is available for a subset of the `person' objects, used in the layout taster task. Object parts are stored in the struct array part, for example: 
>> rec.objects(2).part(1)

ans =

        class: 'head'
         view: ''
    truncated: 0
     occluded: 0
    difficult: 0
        label: 'PAShead'
     orglabel: 'PAShead'
         bbox: [234 138 257 164]
       bndbox: [1x1 struct]
      polygon: []
         mask: []
     hasparts: 0
         part: []
The format of object parts is identical to that for top-level objects. For the `person' parts in the VOC2009 data, parts are not annotated with view, or truncated/difficult flags. The bounding box of a part is specified in image coordinates in the same way as for top-level objects. Note that the object parts may legitimately extend outside the bounding box of the parent object.

8.2.3 viewanno(imgset)

The viewanno function displays the annotation for images in the image set specified by imgset. Some examples: 
>> viewanno('Main/train');
>> viewanno('Main/car_val');
>> viewanno('Layout/train');
>> viewanno('Segmentation/val');

8.3 Classification Functions

8.3.1 VOCevalcls(VOCopts,id,cls,draw)

The VOCevalcls function performs evaluation of the classification task, computing a precision/recall curve and the average precision (AP) measure. The arguments id and cls specify the results file to be loaded, for example: 
>> [rec,prec,ap]=VOCevalcls(VOCopts,'comp1','car',true);
See example_classifier for further examples. If the argument draw is true, the precision/recall curve is drawn in a figure window. The function returns vectors of recall and precision rates in rec and prec, and the average precision measure in ap.

8.4 Detection Functions

8.4.1 VOCevaldet(VOCopts,id,cls,draw)

The VOCevaldet function performs evaluation of the detection task, computing a precision/recall curve and the average precision (AP) measure. The arguments id and cls specify the results file to be loaded, for example: 
>> [rec,prec,ap]=VOCevaldet(VOCopts,'comp3','car',true);
See example_detector for further examples. If the argument draw is true, the precision/recall curve is drawn in a figure window. The function returns vectors of recall and precision rates in rec and prec, and the average precision measure in ap.

8.4.2 viewdet(id,cls,onlytp)

The viewdet function displays the detections stored in a results file for the detection task. The arguments id and cls specify the results file to be loaded, for example: 
>> viewdet('comp3','car',true)
If the onlytp argument is true, only the detections considered true positives by the VOC evaluation measure are displayed.

8.5 Segmentation Functions

8.5.1 create_segmentations_from_detections(id,confidence)

This function creates segmentation results from detection results. create_segmentations_from_detections(id) creates segmentations from the detection results with specified identifier e.g. comp3. This is achieved by rendering the bounding box for each detection in class order, so that later classes overwrite earlier classes (e.g. a person bounding box will overwrite an overlapping an aeroplane bounding box). All detections will be used, no matter what their confidence level. create_segmentations_from_detections(id,confidence) does the same, but only detections above the specified confidence will be used. See example_segmenter for an example.


8.5.2 VOCevalseg(VOCopts,id)

The VOCevalseg function performs evaluation of the segmentation task, computing a confusion matrix and segmentation accuracies for the segmentation task. It returns per-class percentage accuracies, the average overall percentage accuracy, and a confusion matrix, for example: 
>> [accuracies,avacc,conf,rawcounts] = VOCevalseg(VOCopts,'comp3')
Accuracies are defined by the intersection/union measure. The optional fourth output `rawcounts' returns an un-normalized confusion matrix containing raw pixel counts. See example_segmenter for another example. This function will also display a table of overall and per-class accuracies.


8.5.3 VOClabelcolormap(N)

The VOClabelcolormap function creates the color map which has been used for all provided indexed images. You should use this color map for writing your own indexed images, for consistency. The size of the color map is given by N, which should generally be set to 256 to include a color for the `void' label.

8.6 Layout Functions


8.6.1 VOCwritexml(rec,path)

The VOCwritexml function writes a MATLAB structure array to a corresponding XML file. It is provided to support the creation of XML results files for the person layout taster. An example of usage can be found in example_layout.

8.6.2 VOCevallayout(VOCopts,id,draw)

The VOCevallayout function performs evaluation of the person layout task, computing a precision/recall curve and the average precision (AP) measure. The arguments id and cls specify the results file to be loaded, for example: 
>> [rec,prec,ap]=VOCevallayout(VOCopts,'comp6',true);
See example_layout for further examples. If the argument draw is true, the precision/recall curve is drawn in a figure window. The function returns vectors of recall and precision rates in rec and prec, and the average precision measure in ap.

Acknowledgements

We gratefully acknowledge the following, who spent many long hours providing annotation for the VOC2009 database: Jan Hendrik Becker, Patrick Buehler, Kian Ming Adam Chai, Miha Drenik, Chris Engels, Hedi Harzallah, Nicolas Heess, Sam Johnson, Markus Mathias, Alastair Moore, Maria-Elena Nilsback, Patrick Ott, Florian Schroff, Alexander Sorokin, Paul Sturgess, David Tingdahl. We also like thank Andrea Vedaldi for additional assistance. The preparation and running of this challenge is supported by the EU-funded PASCAL2 Network of Excellence on Pattern Analysis, Statistical Modelling and Computational Learning.

Bibliography

1
The PASCAL Visual Object Classes Challenge (VOC2009).
http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2009/index.html.

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The PASCAL Visual Object Classes Challenge 2009 (VOC2009) Development Kit

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