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Article overview
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Indexing of CNN Features for Large Scale Image Search | Ruoyu Liu
; Yao Zhao
; Shikui Wei
; Zhenfeng Zhu
; Lixin Liao
; Shuang Qiu
; | Date: |
2 Aug 2015 | Abstract: | Convolutional neural network (CNN) feature that represents an image with a
global and high-dimensional vector has shown highly discriminative capability
in image search. Although CNN features are more compact than most of local
representation schemes, it still cannot efficiently deal with large-scale image
search issues due to its non-negligible computational cost and storage usage.
In this paper, we propose a simple but effective image indexing framework to
improve the computational and storage efficiency of CNN features. Instead of
projecting each CNN feature vector into a global hashing code, the proposed
framework adapts Bag-of-Word model and inverted table to global feature
indexing. To this end, two strategies, which are based on semantic information
associated with CNN features, are proposed to convert a global vector to one or
several discrete words. In addition, several strategies for compensating
quantization error are fully investigated under the indexing framework.
Extensive experimental results on two public benchmarks show the superiority of
our framework. | Source: | arXiv, 1508.0217 | Services: | Forum | Review | PDF | Favorites |
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