Deep multi query image retrieval |
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| Affiliation: | 1. School of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China;2. Nanjing University of Information Science and Technology, Nanjing 210044, China;1. Universidad Técnica Federico Santa María, Av. España 1680, CP 110-V Valparaíso, Chile;2. Department of Computer Science, TU Dortmund University, Germany;1. School of Innovation and Entrepreneurship, Dalian University of Technology, Dalian, Liaoning 116024, China;2. School of Computer Science and Technology, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China;3. School of Control Science and Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China;1. State Key Lab of CAD&CG, College of Computer Science, Zhejiang University, Hangzhou, China;2. Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science, Zhejiang University, Hangzhou, China |
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| Abstract: | There exist few studies investigating the multi-query image retrieval problem. Existing methods are not based on hash codes. As a result, they are not efficient and fast. In this study, we develop an efficient and fast multi-query image retrieval method when the queries are related to more than one semantic. Image hash codes are generated by a deep hashing method. Consequently, the method requires lower storage space, and it is faster compared to the existing methods. The retrieval is based on the Pareto front method. Reranking performed on the retrieved images by using non-binary deep-convolutional features increase retrieval accuracy considerably. Unlike previous studies, the method supports an arbitrary number of queries. It outperforms similar multi-query image retrieval studies in terms of retrieval time and retrieval accuracy. |
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| Keywords: | Hashing Pareto optimization Multi-query image retrieval |
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