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Simultaneous Feature Aggregating and Hashing for Large-Scale Image Search
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
In most state-of-the-art hashing-based visual search systems, local image descriptors of an image are first aggregated as a single feature vector. This feature vector is then subjected to a hashing function that produces a binary hash code. In previous work, the aggregating and the hashing processes are designed independently. In this paper, we propose a novel framework where feature aggregating and hashing are designed simultaneously and optimized jointly. Specifically, our joint optimization
doi:10.1109/cvpr.2017.449
dblp:conf/cvpr/DoTPC17
fatcat:epfuucbpibeuhbo5gog5lmh4d4