期刊
IEEE TRANSACTIONS ON MULTIMEDIA
卷 17, 期 7, 页码 1006-1018出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2015.2425651
关键词
Feature fusion; fingerprinting; hashing; multiple kernel learning; near-duplicate detection
资金
- National Basic Research Program (973 Program) of China [2011CB302305]
- National Natural Science Foundation of China [61232004]
- EU under Project xLiMe
Image fingerprinting is regarded as an alternative approach to watermarking in terms of near-duplicate detection application. It consists of feature extraction and feature indexing. Generally, the former is mainly related to discrimination, robustness, and security while the latter closely focuses on the efficiency of fingerprints search. To enable fast fingerprints searching over a very large database, we propose a new kernelized multiple feature hashing method to convert the real-value fingerprints into compact binary-value fingerprints. During the process of converting, the proposed hashing method jointly utilizes the kernel trick and multiple feature fusion strategy to map the image represented by multiple features into a compact binary code. With the help of the kernel function, the hashing method can be applied to any format (such as string, graph, set, and so on) as long as there is an associated kernel function available for similarity measurement. In addition, taking multiple features into account aims at improving the discriminability since these multiple evidences are complementary to each other. The extensive experimental results show that the proposed algorithm outperforms state-of-the-art kernelized hashing methods by up to 10 percent.
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