ABSTRACT
This chapter addresses image database modeling in general and, in particular, focuses on developing a hidden semantic concept discovery methodology to address effective semantics-intensive image data mining and retrieval. In the approach proposed in this chapter, 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 region-based representation is achieved. With this representation a probabilistic model based on the statistical-hidden-class assumptions of the image database is obtained, to which the Expectation-Maximization (EM) technique is applied to discover and analyze semantic concepts hidden in the database. An elaborated mining and retrieval algorithm is designed to support the probabilistic model. The semantic similarity is measured through integrating the posterior probabilities of the transformed query image, as well as a constructed negative example, to the discovered semantic concepts. The proposed approach has a solid statistical foundation; the experimental evaluations on a database of 10,000 general-purpose images demonstrate the promise and the effectiveness of the proposed approach.
