Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 40, Issue 5, Pages 1154-1166Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2017.2676779
Keywords
Image retrieval; inverted index; index embedding; SIFT; CNN feature
Funding
- 973 Program [2015CB351803]
- NSFC [61325009, 61390514, 61472378, 61632019, 61429201]
- Natural Science Foundation of Anhui Province [1508085MF109]
- Fundamental Research Funds for the Central Universities
- ARO [W911NF-15-1-0290]
- NEC Laboratories of America
- Blippar
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In content-based image retrieval, SIFT feature and the feature from deep convolutional neural network (CNN) have demonstrated promising performance. To fully explore both visual features in a unified framework for effective and efficient retrieval, we propose a collaborative index embedding method to implicitly integrate the index matrices of them. We formulate the index embedding as an optimization problem from the perspective of neighborhood sharing and solve it with an alternating index update scheme. After the iterative embedding, only the embedded CNN index is kept for on-line query, which demonstrates significant gain in retrieval accuracy, with very economical memory cost. Extensive experiments have been conducted on the public datasets with million-scale distractor images. The experimental results reveal that, compared with the recent state-of-the-art retrieval algorithms, our approach achieves competitive accuracy performance with less memory overhead and efficient query computation.
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