期刊
PATTERN RECOGNITION LETTERS
卷 130, 期 -, 页码 64-72出版社
ELSEVIER
DOI: 10.1016/j.patrec.2018.08.010
关键词
Visual saliency; Complex image; Image retrieval; Feature extraction
资金
- Basic Science Research Program through National Research Foundation of Korea(NRF) - Ministry of Education [2017R1A6A1A03015496]
- National Research Foundation of Korea(NRF) - Korea government(Ministry of Science and ICT) [2017R1E1A1A01077913]
- National Research Foundation of Korea [2017R1E1A1A01077913] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Compared with the traditional text data, multimedia data are concise and contains rich meanings, so people are more willing to use the multimedia data to store information. How to effectively retrieve information is essential. This paper proposes a novel visual saliency guided complex image retrieval model. Initially, Itti visual saliency model is presented. In this model, the overall saliency map is generated by the integration of direction, intensity and color saliency map, respectively. Then, to help describe the image pattern more clearly, we present the multi-feature fusion paradigm of images. To address the complexity of the images, we propose a two-stage definition: (1) Cognitive load based complexity; (2) Cognitive level of complexity classification. The group sparse logistic regression model is integrated to finalize the image retrieval system. The performance of the proposed system is tested on different databases compared with the other state-of-the-art models which overcome the baselines in complex scenarios. (C) 2018 Elsevier B.V. All rights reserved.
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