Fusing shape and appearance information for object category detection
Andreas Opelt, Axel Pinz and Andrew Zisserman
In: BMVC 2006, 4-7 September 2006, Edinburgh, UK.

Abstract

We present a method which is able to combine various feature types (e.g. image patches and edge boundaries) to learn models for object categories. Our objective is to detect object instances in an image, as opposed to the easier task of image categorization. We investigate two algorithms for learning and detecting object categories. Both algorithms benefit from combining features. The first uses a naive combination method for detectors each employing only one type of feature, the second learns the best features (from a pool of patches and boundaries). In experiments we achieve comparable results to the state of the art over a number of datasets, and for some categories we even achieve the lowest errors that have been reported so far. The results also show that certain object categories prefer certain feature types (e.g. boundary fragments for airplanes).

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