PASCAL VOC Challenge

http://grunt1223.iteye.com/blog/970449

在计算视觉的领域中，Pascal VOC Challenge 就好比是数学中的哥德巴赫猜想一样。Pascal的全称是Pattern Analysis, Statical Modeling and Computational Learning。每年，该组织都会提供一系列类别的、带标签的图片，挑战者通过设计各种精妙的算法，仅根据分析图片内容来将其分类，最终通过准确率、召回率、效率来一决高下。 

这项活动从2005年开始，每年的样本数据库都有所不同： 

Year	Statistics	New developments	Notes
2005	Only 4 classes: bicycles, cars, motorbikes, people. Train/validation/test: 1578 images containing 2209 annotated objects.	Two competitions: classification and detection	Images were largely taken from exising public datasets, and were not as challenging as the flickr images subsequently used. This dataset is obsolete.
2006	10 classes: bicycle, bus, car, cat, cow, dog, horse, motorbike, person, sheep. Train/validation/test: 2618 images containing 4754 annotated objects.	Images from flickr and from Microsoft Research Cambridge (MSRC) dataset	The MSRC images were easier than flickr as the photos often concentrated on the object of interest. This dataset is obsolete.
2007	20 classes:Person: personAnimal: bird, cat, cow, dog, horse, sheepVehicle: aeroplane, bicycle, boat, bus, car, motorbike, trainIndoor: bottle, chair, dining table, potted plant, sofa, tv/monitorTrain/validation/test: 9,963 images containing 24,640 annotated objects.	Number of classes increased from 10 to 20Segmentation taster introducedPerson layout taster introducedTruncation flag added to annotationsEvaluation measure for the classification challenge changed to Average Precision. Previously it had been ROC-AUC.	This year established the 20 classes, and these have been fixed since then. This was the final year that annotation was released for the testing data.
2008	20 classes. The data is split (as usual) around 50% train/val and 50% test. The train/val data has 4,340 images containing 10,363 annotated objects.	Occlusion flag added to annotationsTest data annotation no longer made public.The segmentation and person layout data sets include images from the corresponding VOC2007 sets.
2009	20 classes. The train/val data has 7,054 images containing 17,218 ROI annotated objects and 3,211 segmentations.	From now on the data for all tasks consists of the previous years' images augmented with new images. In earlier years an entirely new data set was released each year for the classification/detection tasks.Augmenting allows the number of images to grow each year, and means that test results can be compared on the previous years' images.Segmentation becomes a standard challenge (promoted from a taster)	No difficult flags were provided for the additional images (an omission).Test data annotation not made public.
2010	20 classes. The train/val data has 10,103 images containing 23,374 ROI annotated objects and 4,203 segmentations.	Action Classification taster introduced.Associated challenge on large scale classification introduced based on ImageNet.Amazon Mechanical Turk used for early stages of the annotation.	Method of computing AP changed. Now uses all data points rather than TREC style sampling.Test data annotation not made public.

以一张人物肖像为例，对应的Annotation描述为下： 

 

 <annotation>
     <folder>VOC2007</folder>
     <filename>000001.jpg</filename>
     <source>
         <database>The VOC2007 Database</database>
         <annotation>PASCAL VOC2007</annotation>
         <image>flickr</image>
         <flickrid>341012865</flickrid>
     </source>
     <owner>
         <flickrid>Fried Camels</flickrid>
         <name>Jinky the Fruit Bat</name>
     </owner>
     <size>
         <width>353</width>
         <height>500</height>
         <depth>3</depth>
     </size>
     <segmented>0</segmented>
     <object>
         <name>dog</name>
         <pose>Left</pose>
         <truncated>1</truncated>
         <difficult>0</difficult>
         <bndbox>
             <xmin>48</xmin>
             <ymin>240</ymin>
             <xmax>195</xmax>
             <ymax>371</ymax>
         </bndbox>
     </object>
     <object>
         <name>person</name>
         <pose>Left</pose>
         <truncated>1</truncated>
         <difficult>0</difficult>
         <bndbox>
             <xmin>8</xmin>
             <ymin>12</ymin>
             <xmax>352</xmax>
             <ymax>498</ymax>
         </bndbox>
     </object>
 </annotation>