Image database classification based on concept vector model

Automatic semantic classification of image databases is very useful for users ’ searching and browsing, but it is at the same time a very challenging research problem as well. In this paper, we develop a hidden semantic concept discovery methodology to address effective semantics-intensive image database classification. In our approach, each image in the database is segmented into regions associated with homogenous color, texture, and shape features. By exploiting regional statistical information in each image and employing a vector quantization method, a uniform and sparse regionbased representation is achieved. With this representation a probabilistic model based on statistical-hidden-class assumptions of the image database is obtained, to which the Expectation-Maximization (EM) technique is applied to analyze semantic concepts hidden in the database. Two methods are proposed to utilize the semantic concepts discovered from the probabilistic model for unsupervised and supervised image database classifications, respectively, based on the automatically learned concept vectors. It is shown that the concept vectors are more reliable and robust than the low level features. The developed methodology has a solid statistical foundation; the theoretic analysis and the experimental evaluations on a database of 10,000 generalpurpose images demonstrate its promise of the effectiveness. 1