The PASCAL Visual Object Classes Challenge 2007 (VOC2007) 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 two main tasks: In addition, there are two ``taster'' tasks operating on a subset of the provided data:

2 Data

The VOC2007 database contains a total of 9,963 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. After completion of the challenge, annotation for the test data will be released.

2.1 Classification/Detection Image Sets

For the main tasks - classification and detection, 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. In total there are 9,963 images, containing 24,640 annotated objects.
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		 112 151 126 155 238 306 - -
Bicycle 116 176 127 177 243 353 - -
Bird 180 243 150 243 330 486 - -
Boat 81 140 100 150 181 290 - -
Bottle 139 253 105 252 244 505 - -
Bus 97 115 89 114 186 229 - -
Car 376 625 337 625 713 1250 - -
Cat 163 186 174 190 337 376 - -
Chair 224 400 221 398 445 798 - -
Cow 69 136 72 123 141 259 - -
Diningtable 97 103 103 112 200 215 - -
Dog 203 253 218 257 421 510 - -
Horse 139 182 148 180 287 362 - -
Motorbike 120 167 125 172 245 339 - -
Person 1025 2358 983 2332 2008 4690 - -
Pottedplant 133 248 112 266 245 514 - -
Sheep 48 130 48 127 96 257 - -
Sofa 111 124 118 124 229 248 - -
Train 127 145 134 152 261 297 - -
Tvmonitor 128 166 128 158 256 324 - -
Total 2501 6301 2510 6307 5011 12608 - -

2.2 Segmentation Taster Image Sets

For the segmentation taster task, corresponding image sets are provided as in the classification/detection tasks. These image sets are subsets of those 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. 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.
Table 2: Statistics of the segmentation taster image sets.

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

Aeroplane		 12 17 13 16 25 33 - -
Bicycle 11 16 10 16 21 32 - -
Bird 13 15 13 20 26 35 - -
Boat 11 15 9 29 20 44 - -
Bottle 17 30 13 28 30 58 - -
Bus 14 16 11 15 25 31 - -
Car 14 34 17 36 31 70 - -
Cat 15 15 15 18 30 33 - -
Chair 26 52 20 48 46 100 - -
Cow 11 27 10 16 21 43 - -
Diningtable 14 15 17 17 31 32 - -
Dog 17 20 14 19 31 39 - -
Horse 15 18 17 19 32 37 - -
Motorbike 11 15 15 16 26 31 - -
Person 92 194 79 154 171 348 - -
Pottedplant 17 33 17 45 34 78 - -
Sheep 8 41 13 22 21 63 - -
Sofa 17 22 13 15 30 37 - -
Train 8 14 15 17 23 31 - -
Tvmonitor 20 24 13 16 33 40 - -
Total 209 633 213 582 422 1215 - -

2.3 Person Layout Taster Image Sets

For the person layout taster task, corresponding image sets are provided as in the classification/detection tasks. These image sets are subsets of those for the main tasks, for which all people have been annotated with part layout (head, hands, feet). Table 3 summarizes the number of `person' objects annotated with layout for each image set. Participants are free to additionally use any images and/or annotation in the main trainval set supplied for the classification/detection tasks. For example, participants treating this task as a two stage ``detect then estimate layout'' task may use the additional examples of people to train the detection stage.
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		 166 220 156 219 322 439 - -
All 318 - 328 - 646 - - -


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 Taster Ground Truth

Figure 1: Example of segmentation taster 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 2.5 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.

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. All `person' objects in the image sets used in the person layout taster are annotated with parts, and 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. Note that the use of precision/recall differs from the ROC analysis used in VOC2006 - see section 3.4. 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:
    000004 0.702732
    000006 0.870849
    000008 0.532489
    000018 0.477167
    000019 0.112426
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. Note that this differs from the VOC2006 evaluation measure. Comparison of VOC2006 results using AP and area under ROC curve (AUC) show the same average ranking of methods, but it has been decided that precision/recall analysis gives more intuitive and sensitive evaluation than the ROC analysis used in VOC2006. 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 000006, multiple objects are detected: 
comp3_det_test_car.txt:
    000004 0.702732 89 112 516 466
    000006 0.870849 373 168 488 229
    000006 0.852346 407 157 500 213
    000006 0.914587 2 161 55 221
    000008 0.532489 175 184 232 201
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 Taster

5.1 Task

For each test image pixel, predict the class of the object containing that pixel or 'background' if the object 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

A single competition is defined:
No. Task Training data Test data
5 Segmentation trainval test
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'. Both the images with and without segmentation provided may be used if desired. Participants are not permitted to perform additional manual annotation of either training or test data.

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 example segmenter implementation (section 7.2.3) includes code for generating results in the required format. Along with the submitted image files, participants should also state whether their method used segmentation training data only or both segmentation and bounding box training data.

5.4 Evaluation

The segmentation taster task will be judged by average segmentation accuracy across the twenty classes and the background class. The segmentation accuracy for a class is the number of correctly labeled pixels of that class, divided by the total number of pixels of that class in the ground truth labeling. 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 (if any) detect the person, predicting the bounding box of the person, the presence/absence of parts (head/hands/feet), and the bounding boxes of those parts. Each person detection should be output with an associated real-valued confidence of the detection so that a precision/recall curve can be drawn. This task is an extension of the detection task for the `person' class only. To be considered a correct detection, the predicted bounding box of the person, the parts predicted to be present, and the predicted bounding boxes of those parts, must all be correct.

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
6 Layout trainval test
7 Layout any but VOC test test
In competition 6, 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 7, 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>
        ... detection 1 ...
    </layout>
    <layout>
        ... detection 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>000005</image>
        <confidence>0.8</confidence>
        <bndbox>
            <xmin>1</xmin>
            <ymin>160</ymin>
            <xmax>160</xmax>
            <ymax>352</ymax>
        </bndbox>
        <part>
            <class>head</class>
            <bndbox>
                <xmin>102.0885</xmin>
                <ymin>160.6685</ymin>
                <xmax>147.6362</xmax>
                <ymax>212.7389</ymax>
            </bndbox>
        </part>
        <part>
            <class>hand</class>
            <bndbox>
                <xmin>91.2736</xmin>
                <ymin>262.1854</ymin>
                <xmax>119.3386</xmax>
                <ymax>283.1362</ymax>
            </bndbox>
        </part>
    </layout>
The <image> element specifies the image identifier. The <confidence> element specifies the confidence of the detection, used to generate a precision/recall curve as in the detection task. The <bndbox> element specifies the predicted bounding box for the person. Each <part> element specifies the detection of a particular part of the person e.g. head/hand. 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 predicted 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 detection and corresponding layout prediction must satisfy three 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/VOC2007/           % your results on VOC2007
    VOCdevkit/results/VOC2006/           % your results on VOC2006
    VOCdevkit/local/                     % example code temp dirs
    VOCdevkit/VOC2007/ImageSets          % image sets
    VOCdevkit/VOC2007/Annotations        % annotation files
    VOCdevkit/VOC2007/JPEGImages         % images
    VOCdevkit/VOC2007/SegmentationObject % segmentations by object
    VOCdevkit/VOC2007/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 VOC object class a simple (and not very successful!) detector and layout predictor 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.

7.3 Non-MATLAB Users

For non-MATLAB users, the file formats used for the VOC2007 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 VOC2007/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 VOC2007/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: 
 000601 -1
 000604  0
 000610  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 output of the classifier for this image does not affect its evaluation.
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 Taster Image Sets

The VOC2007/ImageSets/Segmentation/ directory contains text files specifying lists of images for the segmentation 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. 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

For the person layout taster task, participants should use the image sets provided for the main classification/detection tasks.

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 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.

8.2.1.1 Running on VOC2006 test set.

The flag VOC2006 defined at the start of the VOCinit.m script specifies whether the VOC2006 or VOC2007 data should be used. This changes the directories used for image sets and images and the results directory. To run on the VOC2006 test set, set the flag to ``true'' as indicated in the script.

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,'000058'))

rec =

       folder: 'VOC2007'
     filename: '000058.jpg'
       source: [1x1 struct]
         size: [1x1 struct]
    segmented: 0
      imgname: 'VOC2007/JPEGImages/000058.jpg'
      imgsize: [500 375 3]
     database: 'The VOC2007 Database'
      objects: [1x4 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 (VOC2007). 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: 375
     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(1)

ans =

        class: 'person'
         view: ''
    truncated: 0
    difficult: 0
        label: 'PASperson'
     orglabel: 'PASperson'
         bbox: [334 1 436 373]
       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 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(1).bndbox

ans =

    xmin: 334
    ymin: 1
    xmax: 436
    ymax: 373
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 VOC2007 data. The hasparts field specifies if the object has sub-object ``parts'' annotated. For the VOC2007 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(1).part(1)

ans =

        class: 'head'
         view: ''
    truncated: 0
    difficult: 0
        label: 'PAShead'
     orglabel: 'PAShead'
         bbox: [337 2 382 66]
       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 VOC2007 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 accuracies, average overall accuracy and a confusion matrix, for example: 
>> [accuracies,avacc,conf] = VOCevalseg(VOCopts,'comp3')
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,cls,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]=VOCpr(VOCopts,'comp6','person',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 VOC2007 database: Moray Allan, Patrick Buehler, Terry Herbert, Anitha Kannan, Julia Lasserre, Alain Lehmann, Mukta Prasad, Till Quack, John Quinn, Florian Schroff. We are also grateful to James Philbin and Ondra Chum for additional assistance. The preparation and running of this challenge is supported by the EU-funded PASCAL Network of Excellence on Pattern Analysis, Statistical Modelling and Computational Learning.

Bibliography

1
The PASCAL Visual Object Classes Challenge (VOC2007).
http://www.pascal-network.org/challenges/VOC/voc2007/index.html.

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The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Development Kit

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