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Abstract : |
Grouping images into (semantically) meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval. Based on these groupings, effective indices can be built for an image database. In this paper, we cast the image classification problem in a Bayesian framework. Specifically, we consider city vs. landscape classification and further classification of landscape images into sunset, forest, and mountain classes. We demonstrate how high-level concepts can be understood from specific low-level image features under the constraint that the test images do belong to one of the classes in concern. We further demonstrate that a small codebook (the optimal size of codebook is selected using MDL principle) extracted from a vector quantizer can be used to estimate the class-conditional densities needed for the Bayesian methodology. Classification based on color histograms, color coherence vectors, edge direction histograms, and edge direction coherence vectors as features shows promising results. On a database of 2; 716 city and landscape images, our system achieved an accuracy of 95:3 % for city vs. landscape classification. On a subset of 528 landscape images, our system achieves an accuracy of 94:9 % for sunset vs. forest & mountain classification and 93:6 % for forest vs. mountain classification. Our final goal is to combine multiple 2-class classifiers into a single hierarchical classifier. 1, |
