Expert system for color image retrieval |
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| Affiliation: | 1. Center for Cognitive Science, Yonsei University, 134 Shinchon-Dong, Seodaemun-Ku, Seoul 120-749, South Korea;2. Department of Industrial Systems and Information Engineering, Korea University, Sungbuk-gu Anam-dong 5 Ga 1, Seoul 136-701, South Korea;1. Department of Aeronautical Engineering, São Carlos School of Engineering, University of São Paulo, Av. João Dagnone, 1100, 13573-120 São Carlos, SP, Brazil;2. KU Leuven, Department of Mechanical Engineering – PMA Division, Celestijnenlaan 300 B, B-3001 Heverlee, Belgium;3. UDESC, Santa Catarina State University, Department of Mechanical Engineering, Rua Paulo Malschitzki, 200, 89.219-710, Joinville, Santa Catarina, Brazil;1. Faculty of Mechanical and Aerospace Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 143-747, Republic of Korea;2. School of Mechanical and Aerospace Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-742, Republic of Korea;3. Institute of Advanced Machines and Design, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-742, Republic of Korea;1. Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, USA;2. Tecgraf, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil;1. Departamento de Ingeniería, Universidad Nacional del Sur, Av. Alem 1253, 8000, Bahía Blanca, Argentina;2. CONICET, Argentina;3. CeReDeTeC, FRM, Universidad Tecnológica Nacional, Rodríguez 273, 5500, Mendoza, Argentina |
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| 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. |
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