Relevance Feedback in Content-based Image Search
Hao Ran Zhang · 2001
Abstract: Content-based image retrieval (CBIR) is a research area dedicated to address the retrieve and search multimedia documents for digital libraries. Relevance feedback is a powerful technique in CBIR and has been an active research topic for the past few years. In this paper, we review the current state-of-the-art of research on relevance feedbacks for CBIR and present the iFind system developed at Microsoft Research China equipped with a set of powerful relevance feedback algorithms. We also provide an outlook on the remaining research issues in CBIR, especially on applying learning and data mining technologies in search of multimedia data on the Web. 1. Introduction: The Challenges in Content-Based Image Retrieval Efficient image indexing and access tools are essential for efficient utilization of this massive digital resource and substantial research efforts have been devoted to address this issue. However, by and large, the earlier image database systems used in digital libraries [e.g. 1] have all taken keyword or text-based approaches for indexing and