期刊
IEEE TRANSACTIONS ON MULTIMEDIA
卷 16, 期 3, 页码 601-611出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2014.2301979
关键词
Binary signature; cascaded hashing; matching verification; mobile image search
资金
- Fundamental Research Funds for the Central Universities [WK2100060014]
- University of Science and Technology of China [KY2100000036]
- NSFC [61325009, 61390514, 61272316]
- ARO [W911NF-12-1-0057]
- NEC Laboratories of America
- FXPAL
- UTSA START-R Research Award
- National Science Foundation of China (NSFC) [61128007]
State-of-the-art image retrieval algorithms using local invariant features mostly rely on a large visual codebook to accelerate the feature quantization and matching. This codebook typically contains millions of visual words, which not only demands for considerable resources to train offline but also consumes large amount of memory at the online retrieval stage. This is hardly affordable in resource limited scenarios such as mobile image search applications. To address this issue, we propose a codebook-free algorithm for large scale mobile image search. In our method, we first employ a novel scalable cascaded hashing scheme to ensure the recall rate of local feature matching. Afterwards, we enhance the matching precision by an efficient verification with the binary signatures of these local features. Consequently, our method achieves fast and accurate feature matching free of a huge visual codebook. Moreover, the quantization and binarizing functions in the proposed scheme are independent of small collections of training images and generalize well for diverse image datasets. Evaluated on two public datasets with a million distractor images, the proposed algorithm demonstrates competitive retrieval accuracy and scalability against four recent retrieval methods in literature.
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