Adapted Gaussian models for image classification

Authors:
M. Dixit;N. Rasiwasia;N. Vasconcelos
Affiliations:
Dept. of Electr. & Comput. Eng., Univ. of California, San Diego, CA, USA;Dept. of Electr. & Comput. Eng., Univ. of California, San Diego, CA, USA;Dept. of Electr. & Comput. Eng., Univ. of California, San Diego, CA, USA
Venue:
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Year:
2011

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Abstract

A general formulation of "Bayesian Adaptation" for generative and discriminative classification in the topic model framework is proposed. A generic topic-independent Gaussian mixture model, known as the background GMM, is learned using all available training data and adapted to the individual topics. In the generative framework, a Gaussian variant of the spatial pyramid model is used with a Bayes classifier. For the discriminative case, a novel predictive histogram representation for an image is presented. This builds upon the adapted topic model structure, using the individual class dictionaries and Bayesian weighting. The resulting histogram representation is evaluated for classification using a Support Vector Machine (SVM). A comparative evaluation of the proposed image models with the standard ones in the image classification literature is provided on three benchmark datasets.