Content-based image retrieval using local visual attention feature |
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| Affiliation: | 1. School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, PR China;2. Jiangsu Key Laboratory of Image and Video Understanding for Social Safety, Nanjing University of Science and Technology, Nanjing 210094, PR China;1. School of Mechanical and Electronic Engineering, Jingdezhen Ceramic Institute, Jiangxi 333403, China;2. School of Information Science and Technology, Sun Yat-Sen University, Guangdong 510275, China;3. School of Electronic and Information Engineering, South China University of Technology, Guangdong 510006, China;1. School of Mathematics, Georgia Institute of Technology, 686 Cherry Street NW, Atlanta, GA 30332, USA;2. Istituto per le Applicazioni del Calcolo, CNR, Via dei Taurini 19, 00185 Roma, Italy;1. School of Information and Engineering, Zhengzhou University, Zhengzhou, China;2. Electrical and Computer Engineering, Ryerson University, Toronto, Canada;1. School of Computer Engineering, KIIT University, Bhubaneshwar, India;2. Department of Physical Sciences, Indian Institute of Science Education and Research (IISER), Kolkata, India;1. School of Computer and Control Engineering, University of Chinese Academy of Sciences, 100049 Beijing, China;2. Institute of Automation, Chinese Academy of Sciences, Beijing, China;3. College of Computer Science and Technology, Beijing University of Technology, 100124 Beijing, China;4. National Engineering Research Center for Multimedia Software, School of Computer, Wuhan University, 430072 Wuhan, China;5. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, P.O. Box 2728, Beijing, China;6. Key Lab of Intell. Info. Process, Institute of Computing Technology, Chinese Academy of Sciences, 100190 Beijing, China |
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| Abstract: | Content-based image retrieval (CBIR) has been an active research topic in the last decade. As one of the promising approaches, salient point based image retrieval has attracted many researchers. However, the related work is usually very time consuming, and some salient points always may not represent the most interesting subset of points for image indexing. Based on fast and performant salient point detector, and the salient point expansion, a novel content-based image retrieval using local visual attention feature is proposed in this paper. Firstly, the salient image points are extracted by using the fast and performant SURF (Speeded-Up Robust Features) detector. Then, the visually significant image points around salient points can be obtained according to the salient point expansion. Finally, the local visual attention feature of visually significant image points, including the weighted color histogram and spatial distribution entropy, are extracted, and the similarity between color images is computed by using the local visual attention feature. Experimental results, including comparisons with the state-of-the-art retrieval systems, demonstrate the effectiveness of our proposal. |
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| Keywords: | Image retrieval Salient point SURF Visually significant image point Weighted color histogram Spatial distribution entropy Color complexity measure Similarity |
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