Manifold-ranking embedded order preserving hashing for image semantic retrieval |
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Affiliation: | 1. Department of Computer Science, City University of Hong Kong, Hong Kong;2. Department of Computer Science and Engineering, HITEC University, Taxila, Pakistan;3. School of Information Science and Engineering, Shandong University, China;1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China;2. School of Computer Science and Technology, Huaqiao University, Xiamen 361021, China;3. Institute of Systems Engineering, Macau University of Science and Technology, Taipa, Macau;4. School of Electro-Mechanical Engineering, Xidian University, Xi’an 710071, China;5. Advanced Manufacturing Institute, King Saud University, Riyadh 11421, Saudi Arabia;6. Industrial Engineering Department, College of Engineering, King Saud University, Saudi Arabia |
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Abstract: | Due to the storage and computational efficiency of hashing technology, it has proven a valuable tool for large scale similarity search. In many cases, the large scale data in real-world lie near some (unknown) low-dimensional and non-linear manifold. Moreover, Manifold Ranking approach can preserve the global topological structure of the data set more effectively than Euclidean Distance-based Ranking approach, which fails to preserve the semantic relevance degree. However, most existing hashing methods ignore the global topological structure of the data set. The key issue is how to incorporate the global topological structure of data set into learning effective hashing function. In this paper, we propose a novel unsupervised hashing approach, namely Manifold-Ranking Embedded Order Preserving Hashing (MREOPH). A manifold ranking loss is introduced to solve the issue of global topological structure preserving. An order preserving loss is introduced to ensure the consistency between manifold ranking and hamming ranking. A hypercubic quantization loss is introduced to learn discrete binary codes. The information theoretic regularization term is taken into consideration for preserving desirable properties of hash codes. Finally, we integrate them in a joint optimization framework for minimizing the information loss in each processing. Experimental results on three datasets for semantic search clearly demonstrate the effectiveness of the proposed method. |
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Keywords: | Manifold ranking Hashing Image semantic retrieval |
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