Abstract
Recently, as Web and various databases contain a large number of images, content-based image retrieval (CBIR) applications are greatly needed. This paper proposes a new image retrieval system using color-spatial information from those applications. First, this paper suggests two kinds of indexing keys to prune away irrelevant images to a given query image: major colors' set (MCS) signature related with color information and distribution block signature (DBS) related with spatial information. After successively applying these filters to a large database, we get only small amount of high potential candidates that are somewhat similar to a query image. Then we make use of the quad modeling (QM) method to set the initial weights of two-dimensional cell in a query image according to each major color. Finally, we retrieve more similar images from the database by comparing a query image with candidate images through a similarity measuring function associated with the weights. In that procedure, we use a new relevance feedback mechanism. This feedback enhances the retrieval effectiveness by dynamically modulating the weights of color-spatial information. Experiments show that the proposed system is not only efficient but also effective.
Original language | English |
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Pages (from-to) | 347-357 |
Number of pages | 11 |
Journal | Expert Systems With Applications |
Volume | 28 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2005 Feb |
Bibliographical note
Copyright:Copyright 2011 Elsevier B.V., All rights reserved.
Keywords
- CBIR (content-based image retrieval)
- DBS (distribution block signature)
- MCS (major colors' set) signature
- QM (quad modeling)
- Relevance feedback
ASJC Scopus subject areas
- General Engineering
- Computer Science Applications
- Artificial Intelligence