Hidden Semantic Concept Discovery in Region Based Image Retrieval

This paper addresses Content Based Image Retrieval (CBIR), focusing on developing a hidden semantic concept discovery methodology to address effective semanticsintensive image retrieval. In our approach, each image in the database is segmented to 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 statistical-hiddenclass assumptions of the image database is obtained, to which Expectation-Maximization (EM) technique is applied to analyze semantic concepts hidden in the database. An elaborated 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 concep...

• Type: Conference
• Year: 2004
• Where: CVPR
• Authors: Ruofei Zhang, Zhongfei (Mark) Zhang
• Keywords: Computer Vision, CVPR 2004, Elaborated Retrieval Algorithm, Hidden Semantic Concept, Semantic Concepts, Semantic Similarity, Semanticsintensive Image Retrieval