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Improving Image Retrieval Effectiveness via Multiple Queries

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Abstract

Conventional approaches to image retrieval are based on the assumption that relevant images are physically near the query image in some feature space. This is the basis of the cluster hypothesis. However, semantically related images are often scattered across several visual clusters. Although traditional Content-based Image Retrieval (CBIR) technologies may utilize the information contained in multiple queries (gotten in one step or through a feedback process), this is often only a reformulation of the original query. As a result most of these strategies only get the images in some neighborhood of the original query as the retrieval result. This severely restricts the system performance. Relevance feedback techniques are generally used to mitigate this problem. In this paper, we present a novel approach to relevance feedback which can return semantically related images in different visual clusters by merging the result sets of multiple queries. We also provide experimental results to demonstrate the effectiveness of our approach.

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Authors and Affiliations

  1. Department of Computer Science, University of Virginia, Charlottesville, VA, 22904, USA

    Xiangyu Jin & James C. French

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  1. Xiangyu Jin
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  2. James C. French
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Correspondence to Xiangyu Jin.

Additional information

Xiangyu Jin received his B.S. and M.E. in Computer Science from the Nanjing University, China, in 1999 and 2002, respectively. He has a visiting student in Microsoft Research Asia (2001) and now is a Ph.D. candidate in the Department of Computer Science at the University of Virginia. His current research interest includes multimedia information retrieval and user interface study. He had the authored or co-authored about 20 publications in these areas.

James French is currently a Research Associate Professor in the Department of Computer Science at the University of Virginia. He received a B.A. in Mathematics and M.S. and Ph.D. (1982) degrees in Computer Science, all at the University of Virginia. After several years in industry, he returned to the University of Virginia in 1987 as a Senior Scientist in the Institute for Parallel Computation and joined the Department of Computer Science in 1990. His current research interests include content-based retrieval and information retrieval in widely distributed information systems. He is the editor of five books, and the author or co-author of one book and over 75 papers and book chapters. Professor French is a member of the ACM, the IEEE Computer Society, ASIST, and Sigma Xi. At the time of this work he was on a leave of absence from the University of Virginia serving as a program director at the U.S. National Science Foundation.

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Jin, X., French, J.C. Improving Image Retrieval Effectiveness via Multiple Queries. Multimed Tools Appl 26, 221–245 (2005). https://doi.org/10.1007/s11042-005-0453-5

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