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Publication
MIR 2010
Conference paper
Recognition of object categories using affine kernels
Abstract
With the growing image collection on the web, classifying images has become an actively explored problem. In this paper we present a novel approach to the classification of images depicting objects in a category using the odd-man-out principle of visual categorization. Specifically, we build a model of an object category by noting discriminative features that are commonly observed across the member images of the class. Appearance changes due to pose, illumination and intra-class variations are modeled using multi-scale affine kernels. The best matching affine kernel for a given query image is found as the one that has the largest overlap of discriminable features that are commonly observed across the class. We show that using the odd-man-out principle of IQ tests not only results in better feature selection but also in more robust object class categorization, in comparison to the state-of-the-art methods on large benchmark image datasets. Copyright 2010 ACM.