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Concept learning by fuzzy k-NN classification and relevance feedback for efficient image retrieval
Authors:Hossein Nezamabadi-pour  Ehsanollah Kabir
Affiliation:1. Department of Electrical Engineering, Tarbiat Modarres University, P.O. Box 14115-143, Tehran, Iran;2. Department of Electrical Engineering, Shahid Bahonar University of Kerman, P.O. Box 76169-133, Kerman, Iran;1. Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China;2. College of Electronic and Communication Engineering, Tianjin Normal University, China;3. Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing 100044, China;1. CINVESTAV, Unidad Guadalajara, Av. del Bosque 1145, Col. El Bajío, Zapopan, 45015 Jalisco, Mexico;2. Chemical Engineering Department, University of Guadalajara, Blvd. Marcelino García Barragán 1451, Guadalajara, 44430 Jalisco, Mexico;3. Department of Electrical and Information Engineering, Center of Excellence DEWS, University of L''Aquila, Poggio di Roio, 67040 L''Aquila, Italy;1. Centro Universitario de Ciencias Exactas e Ingenierias, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico;2. Facultad de Ciencias Fisico-Matematicas, Universidad Autonoma de Nuevo Leon, San Nicolas de los Garza, Nuevo Leon, Mexico;3. Department of Systems Immunology, Helmholtz Centre for Infection Research, Inhoffenstraße 7, D-38124 Braunschweig, Germany;1. School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China;2. Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, China;3. China Key Laboratory of Applied Mathematics and Complex Systems, Gansu 730000, China;1. Department of Pediatrics, University of Massachusetts Medical School, Worcester, Massachusetts, USA;2. Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, Massachusetts, USA
Abstract:A new method for combining visual and semantic features in image retrieval is presented. A fuzzy k-NN classifier assigns initial semantic labels to database images. These labels are gradually modified by relevance feedbacks from the users. Experimental results on a database of 1000 images from 10 semantic groups are reported.
Keywords:
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