International audienceThe PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). Four object classes were selected: motorbikes, bicycles, cars and people. Twelve teams entered the challenge. In this chapter we provide details of the datasets, algorithms used by the teams, evaluation criteria, and results achieved

The Bag of Visual Words (BoV) paradigm has successfully been applied to image content analysis tasks such as image classification and object detection. The basic BoV approach overlooks spatial descriptor distribution within images. Here we describe spatial extensions to BoV and experimentally compare them in the VOC2007 benchmark image category detection task. In particular, we compare two ways for tiling images geometrically: soft tiling approach-proposed hereand the traditional hard tiling technique. The experiments also address two methods of fusing information from several tilings of the images: post-classifier fusion and fusion on the level of a SVM kernel.The experiments confirm that the performance of a BoV system can be greatly enhanced by taking the descriptors' spatial distribution into account. The soft tiling technique performs well even with a single tiling mask, whereas multimask fusion is necessary for good category detection performance in case of hard tiling. The evaluated fusion mechanisms performed approximately equally well.

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