NDLI: Image hashing basing on joint learning of multi-dimension features

Content Provider	IEEE Xplore Digital Library
Author	Li Huanyu Li Hao Yang Yuan
Copyright Year	2015
Description	Author affiliation: Coll. of ATC Navig., Air Force Eng. Univ., Xi'an, China (Li Hao; Yang Yuan) \|\| Coll. of Aerosp. Eng., Air Force Eng. Univ., Xi'an, China (Li Huanyu)
Abstract	For the problem of image retrieval in computer vision, basing on Principal Component Analysis (PCA) and convolution filtering, a novel image hashing realized by joint learning of multi-dimension features is proposed. As the first step, in order to get the convolution filters, the PCA eigenvectors is learned from the matrix constructed by the patches which are extracted randomly from original images. Next, in order to get the multi-dimension feature expression of the original images, the original images are convolution filtered to several groups which are in accordance with the filter sequence. Then, the hash projection matrix and binary coding are learned by a traditional hashing operator in each dimension respectively. Finally, the hash code of our joint learning model is obtained by merging the grouping binary coding together. Abundant experiment is done to validate the algorithm validity on a widely used dataset called CIFAR-10. The result shows that the algorithm of image hashing proposed in this paper performs well in image retrieval application, compare with the traditional image hashing, there is a certain performance improvement on both precision and recall.
Starting Page	571
Ending Page	576
File Size	693701
Page Count	6
File Format	PDF
e-ISBN	9781467371896
DOI	10.1109/CAC.2015.7382565
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-27
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image hashing Filtering Convolution Merging Image retrieval Feature learning Feature extraction Encoding Convolution filtering Principal component analysis
Content Type	Text
Resource Type	Article

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