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Title: Texture synthesis guided deep hashing for texture image retrieval
Authors: Bhunia A.K.
Perla S.R.K.
Mukherjee P.
Das A.
Pratim Roy, Partha
Published in: Proceedings of 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019
Abstract: With the large scale explosion of images and videos over the internet, efficient hashing methods have been developed to facilitate memory and time efficient retrieval of similar images. However, none of the existing works use hashing to address texture image retrieval mostly because of the lack of sufficiently large texture image databases. Our work addresses this problem by developing a novel deep learning architecture that generates binary hash codes for input texture images. For this, we first pre-train a Texture Synthesis Network (TSN) which takes a texture patch as input and outputs an enlarged view of the texture by injecting newer texture content. Thus it signifies that the TSN encodes the learnt texture specific information in its intermediate layers. In the next stage, a second network gathers the multi-scale feature representations from the TSN's intermediate layers using channel-wise attention, combines them in a progressive manner to a dense continuous representation which is finally converted into a binary hash code with the help of individual and pairwise label information. The new enlarged texture patches from the TSN also help in data augmentation to alleviate the problem of insufficient texture data and are used to train the second stage of the network. Experiments on three public texture image retrieval datasets indicate the superiority of our texture synthesis guided hashing approach over existing state-of-the-art methods. © 2019 IEEE.
Citation: Proceedings of 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019, (2019), 609- 618
Issue Date: 2019
Publisher: Institute of Electrical and Electronics Engineers Inc.
Keywords: Computer graphics
Computer vision
Deep learning
Hash functions
Image retrieval
Input and outputs
Intermediate layers
Large-scale explosions
Learning architectures
Multi-scale features
Specific information
State-of-the-art methods
Texture image retrieval
Image texture
ISBN: 9.78173E+12
Author Scopus IDs: 57188719920
Author Affiliations: Bhunia, A.K., Nanyang Technological University, Singapore, Singapore
Perla, S.R.K., Institute of Engineering and Management, India
Mukherjee, P., Institute of Engineering and Management, India
Das, A., Institute of Engineering and Management, India
Roy, P.P., Indian Institute of Technology Roorkee, India
Appears in Collections:Conference Publications [CS]

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