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Exploiting Class-Specific Features in Multi-feature Dissimilarity Space for Efficient Querying of Images

Yilmaz, Turgay
Yazıcı, Adnan
Yildirim, Yakup
Combining multiple features is an empirically validated approach in the literature, which increases the accuracy in querying. However, it entails processing intrinsic high-dimensionality of features and complicates realizing an efficient system. Two primary problems can be discussed for efficient querying: representation of images and selection of features. In this paper, a class-specific feature selection approach with a dissimilarity based representation method is proposed. The class-specific features are determined by using the representativeness and discriminativeness of features for each image class. The calculations are based on the statistics on the dissimilarity values of training images.