Bayesian framework for semantic classification of outdoor vacation images

Grouping images into (semantically) meaningful categories using low-level visual features is still 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 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 delineated classes. We further demonstrate that a small codebook (the optimal size is selected using the 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 percent for city vs. landscape classification. On a subset of 528 landscape images, our system achieves an accuracy of 94.9 percent for sunset vs. forest and mountain classification, and 93.6 percent for forest vs. mountain classification. Our final goal is to combine multiple 2- class classifiers into a single hierarchical classifier.