Incorporate Discriminant Analysis with EM Algorithm in Image Retrieval

One of the difficulties of Content-Based Image Retrieval (CBIR) is the gap between high-level concepts and low-level image features, e.g., color and texture. Relevance feedback was proposed [1] to take into account of the above characteristics in CBIR. Although relevance feedback incrementally supplies more information for fine retrieval, two challenges exist: (1) the labeled images from the relevance feedback are still very limited compared to the large unlabeled images in the image database. (2) relevance feedback does not offer a specific technique to automatically weight the low-level feature. In this paper, image retrieval is formulated as a transductive learning problem by combining unlabeled images in supervised learning to achieve better classification. Experimental results show that the proposed approach has a satisfactory performance for image retrieval applications.

• Type: Conference
• Year: 2000
• Where: ICMCS
• Authors: Qi Tian, Ying Wu, Thomas S. Huang
• Keywords: ICMCS 2000, Image, Image Retrieval, Multimedia, Relevance Feedback