Journal
IEEE TRANSACTIONS ON BIG DATA
Volume 7, Issue 3, Pages 559-573Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBDATA.2019.2919570
Keywords
Visualization; Encoding; Indexes; Training; Feature extraction; Vector quantization; Big data management; web image search; near-duplicate image search; large-scale; feature quantization; feature coding
Funding
- Canada Research Chair program(CRC) Fund
- AUTO21 Network of Centers of Excellence
- National Natural Science Foundation of China [61602253, U1836208, U1536206, U1836110, 61672294]
- National Key R&D Program of China [2018YFB1003205]
- Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET) fund, China
- MOST through Pervasive Artificial Intelligence Research (PAIR) Labs, Taiwan [108-2634-F-259-001]
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For scalable feature matching in large-scale web image search, a multiple distance-based feature coding scheme is proposed, which does not require visual codebook training and shows stability and discriminability. Experimental results demonstrate the superiority of this approach in comparison to other methods for large-scale web image search using recent feature quantization methods.
For scalable feature matching in large-scale web image search, the bag-of-visual-words-based (BOW) approaches generally code local features as visual words to construct an inverted index file to match features efficiently. Both the popular feature coding techniques, i.e., K-means-based vector quantization and scalar quantization, directly quantize features to generate visual words. K-means-based vector quantization requires expensive visual codebook training, whereas scalar quantization leads to the miss of many matches due to the low stability of individual components of feature vectors. To address the above issues, we demonstrate that the corresponding sub-vectors of similar features generally have similar distances to multiple reference points in feature subspace and propose a multiple distance-based feature coding scheme for scalable feature matching. Specifically, based on the distances between the sub-vectors and multiple distinct reference points, we transform each feature to a set of feature codes, where one code is treated as a visual word required to construct the inverted index file whereas the others are embedded into the index file to further verify the feature matching based on the visual words. The proposed coding scheme does not need visual codebook training and shows desirable stability and discriminability. Moreover, in the matching verification, a feature-distance estimation method is proposed to estimate the Euclidean distances between features for an accurate matching verification. Extensive experimental results demonstrate the superiority of the proposed approach in comparison to the other approaches using recent feature quantization methods for large-scale web image search.
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