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Learning deep unsupervised binary codes for image retrieval
Junjie Chen
,
Kwok Wai Cheung
, Anran Wang
Department of Computer Science
Research output
:
Chapter in book/report/conference proceeding
›
Conference proceeding
›
peer-review
23
Citations (Scopus)
Overview
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Dive into the research topics of 'Learning deep unsupervised binary codes for image retrieval'. Together they form a unique fingerprint.
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Keyphrases
Approximate Nearest Neighbor Search
25%
Binary Codes
100%
Compact Representation
50%
Data Hashing
25%
Decoder
25%
Deep Autoencoder Network
25%
Deep Unsupervised Hashing
50%
Encoder
25%
Hashing
50%
Hashing Method
75%
Image Retrieval
100%
Label Information
25%
Multimedia Input
25%
Multimedia Retrieval
25%
Nonlinear Transformation
25%
Nontrivial Solutions
25%
Poor Generalization
25%
Popular
25%
Product Quantization
25%
Quadratic Constraint
25%
Quantization Error
25%
Retrieval Task
25%
Search Methods
25%
Supervised Learning
25%
Triplet Loss
25%
Unsupervised Hashing
25%
Unsupervised Method
50%
Computer Science
Compact Representation
66%
Deep Autoencoder
33%
Deep Learning Method
100%
Experimental Result
33%
Hashing
100%
Hashing Method
100%
Image Retrieval
100%
Information Loss
33%
Multimedia
33%
Multimedia Retrieval
33%
Neighbour Search
33%
Nonlinear Transformation
33%
Product Quantization
33%
Quantisation Error
33%
Supervised Learning
33%
Trivial Solution
33%
Unsupervised Method
66%