The Experts below are selected from a list of 14814 Experts worldwide ranked by ideXlab platform
Partha Pratim Roy - One of the best experts on this subject based on the ideXlab platform.
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WACV - Texture synthesis guided deep hashing for texture image retrieval
2019 IEEE Winter Conference on Applications of Computer Vision (WACV), 2019Co-Authors: Ayan Kumar Bhunia, Sai Raj Kishore Perla, Pranay Mukherjee, Abhirup Das, Partha Pratim RoyAbstract: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.
Ayan Kumar Bhunia - One of the best experts on this subject based on the ideXlab platform.
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WACV - Texture synthesis guided deep hashing for texture image retrieval
2019 IEEE Winter Conference on Applications of Computer Vision (WACV), 2019Co-Authors: Ayan Kumar Bhunia, Sai Raj Kishore Perla, Pranay Mukherjee, Abhirup Das, Partha Pratim RoyAbstract: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.
Abhirup Das - One of the best experts on this subject based on the ideXlab platform.
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WACV - Texture synthesis guided deep hashing for texture image retrieval
2019 IEEE Winter Conference on Applications of Computer Vision (WACV), 2019Co-Authors: Ayan Kumar Bhunia, Sai Raj Kishore Perla, Pranay Mukherjee, Abhirup Das, Partha Pratim RoyAbstract: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.
Sai Raj Kishore Perla - One of the best experts on this subject based on the ideXlab platform.
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WACV - Texture synthesis guided deep hashing for texture image retrieval
2019 IEEE Winter Conference on Applications of Computer Vision (WACV), 2019Co-Authors: Ayan Kumar Bhunia, Sai Raj Kishore Perla, Pranay Mukherjee, Abhirup Das, Partha Pratim RoyAbstract: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.
Pranay Mukherjee - One of the best experts on this subject based on the ideXlab platform.
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WACV - Texture synthesis guided deep hashing for texture image retrieval
2019 IEEE Winter Conference on Applications of Computer Vision (WACV), 2019Co-Authors: Ayan Kumar Bhunia, Sai Raj Kishore Perla, Pranay Mukherjee, Abhirup Das, Partha Pratim RoyAbstract: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.