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Image/Video Storage, Retrieval

ACTION RECOGNITION USING SPATIO-TEMPORAL DIFFERENTIAL MOTION

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Authors:
Gaurav Kumar Yadav, Amit Sethi
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15 September 2017 - 6:09am
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[1] Gaurav Kumar Yadav, Amit Sethi, "ACTION RECOGNITION USING SPATIO-TEMPORAL DIFFERENTIAL MOTION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2116. Accessed: Sep. 20, 2020.
@article{2116-17,
url = {http://sigport.org/2116},
author = {Gaurav Kumar Yadav; Amit Sethi },
publisher = {IEEE SigPort},
title = {ACTION RECOGNITION USING SPATIO-TEMPORAL DIFFERENTIAL MOTION},
year = {2017} }
TY - EJOUR
T1 - ACTION RECOGNITION USING SPATIO-TEMPORAL DIFFERENTIAL MOTION
AU - Gaurav Kumar Yadav; Amit Sethi
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2116
ER -
Gaurav Kumar Yadav, Amit Sethi. (2017). ACTION RECOGNITION USING SPATIO-TEMPORAL DIFFERENTIAL MOTION. IEEE SigPort. http://sigport.org/2116
Gaurav Kumar Yadav, Amit Sethi, 2017. ACTION RECOGNITION USING SPATIO-TEMPORAL DIFFERENTIAL MOTION. Available at: http://sigport.org/2116.
Gaurav Kumar Yadav, Amit Sethi. (2017). "ACTION RECOGNITION USING SPATIO-TEMPORAL DIFFERENTIAL MOTION." Web.
1. Gaurav Kumar Yadav, Amit Sethi. ACTION RECOGNITION USING SPATIO-TEMPORAL DIFFERENTIAL MOTION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2116

Street-to-Shop Shoe Retrieval with Multi-Scale Viewpoint Invariant Triplet Network


In this paper, we aim to find exactly the same shoes given a daily shoe photo (street scenario) that matches the online shop shoe photo (shop scenario). There are large visual differences between the street and shop scenario shoe images. To handle the discrepancy of different scenarios, we learn a feature embedding for shoes via a viewpoint-invariant triplet network, the feature activations of which reflect the inherent similarity between any two shoe images.

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Authors:
Huijing Zhan, Boxin Shi, Alex C. Kot
Submitted On:
15 September 2017 - 1:04am
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ICIP2017_V6.pptx

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[1] Huijing Zhan, Boxin Shi, Alex C. Kot, "Street-to-Shop Shoe Retrieval with Multi-Scale Viewpoint Invariant Triplet Network", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2082. Accessed: Sep. 20, 2020.
@article{2082-17,
url = {http://sigport.org/2082},
author = {Huijing Zhan; Boxin Shi; Alex C. Kot },
publisher = {IEEE SigPort},
title = {Street-to-Shop Shoe Retrieval with Multi-Scale Viewpoint Invariant Triplet Network},
year = {2017} }
TY - EJOUR
T1 - Street-to-Shop Shoe Retrieval with Multi-Scale Viewpoint Invariant Triplet Network
AU - Huijing Zhan; Boxin Shi; Alex C. Kot
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2082
ER -
Huijing Zhan, Boxin Shi, Alex C. Kot. (2017). Street-to-Shop Shoe Retrieval with Multi-Scale Viewpoint Invariant Triplet Network. IEEE SigPort. http://sigport.org/2082
Huijing Zhan, Boxin Shi, Alex C. Kot, 2017. Street-to-Shop Shoe Retrieval with Multi-Scale Viewpoint Invariant Triplet Network. Available at: http://sigport.org/2082.
Huijing Zhan, Boxin Shi, Alex C. Kot. (2017). "Street-to-Shop Shoe Retrieval with Multi-Scale Viewpoint Invariant Triplet Network." Web.
1. Huijing Zhan, Boxin Shi, Alex C. Kot. Street-to-Shop Shoe Retrieval with Multi-Scale Viewpoint Invariant Triplet Network [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2082

Street-to-Shop Shoe Retrieval with Multi-Scale Viewpoint Invariant Triplet Network


In this paper we aim to find exactly the same shoes given a daily shoe photo (street scenario) that matches the online shop shoe photo (shop scenario). There are large visual differences between the street and shop scenario shoe images. To handle the discrepancy of different scenarios, we learn a feature embedding for shoes via a viewpoint-invariant triplet network, the feature activations of which reflect the inherent similarity between any two shoe images.

Paper Details

Authors:
Huijing Zhan, Boxin Shi, Alex C. Kot
Submitted On:
15 September 2017 - 12:50am
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ICIP2017_V6.pptx

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[1] Huijing Zhan, Boxin Shi, Alex C. Kot, "Street-to-Shop Shoe Retrieval with Multi-Scale Viewpoint Invariant Triplet Network", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2081. Accessed: Sep. 20, 2020.
@article{2081-17,
url = {http://sigport.org/2081},
author = {Huijing Zhan; Boxin Shi; Alex C. Kot },
publisher = {IEEE SigPort},
title = {Street-to-Shop Shoe Retrieval with Multi-Scale Viewpoint Invariant Triplet Network},
year = {2017} }
TY - EJOUR
T1 - Street-to-Shop Shoe Retrieval with Multi-Scale Viewpoint Invariant Triplet Network
AU - Huijing Zhan; Boxin Shi; Alex C. Kot
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2081
ER -
Huijing Zhan, Boxin Shi, Alex C. Kot. (2017). Street-to-Shop Shoe Retrieval with Multi-Scale Viewpoint Invariant Triplet Network. IEEE SigPort. http://sigport.org/2081
Huijing Zhan, Boxin Shi, Alex C. Kot, 2017. Street-to-Shop Shoe Retrieval with Multi-Scale Viewpoint Invariant Triplet Network. Available at: http://sigport.org/2081.
Huijing Zhan, Boxin Shi, Alex C. Kot. (2017). "Street-to-Shop Shoe Retrieval with Multi-Scale Viewpoint Invariant Triplet Network." Web.
1. Huijing Zhan, Boxin Shi, Alex C. Kot. Street-to-Shop Shoe Retrieval with Multi-Scale Viewpoint Invariant Triplet Network [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2081

Provenance Filtering for Multimedia Phylogeny


Departing from traditional digital forensics modeling, which seeks to analyze single objects in isolation, multimedia phylogeny analyzes the evolutionary processes that influence digital objects and collections over time. One of its integral pieces is provenance filtering, which consists of searching a potentially large pool of objects for the most related ones with respect to a given query, in terms of possible ancestors (donors or contributors) and descendants.

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Authors:
Allan Pinto, Daniel Moreira, Aparna Bharati, Joel Brogan, Kevin Bowyer, Patrick Flynn, Walter Scheirer, Anderson Rocha
Submitted On:
14 September 2017 - 10:39pm
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[1] Allan Pinto, Daniel Moreira, Aparna Bharati, Joel Brogan, Kevin Bowyer, Patrick Flynn, Walter Scheirer, Anderson Rocha, "Provenance Filtering for Multimedia Phylogeny", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2072. Accessed: Sep. 20, 2020.
@article{2072-17,
url = {http://sigport.org/2072},
author = {Allan Pinto; Daniel Moreira; Aparna Bharati; Joel Brogan; Kevin Bowyer; Patrick Flynn; Walter Scheirer; Anderson Rocha },
publisher = {IEEE SigPort},
title = {Provenance Filtering for Multimedia Phylogeny},
year = {2017} }
TY - EJOUR
T1 - Provenance Filtering for Multimedia Phylogeny
AU - Allan Pinto; Daniel Moreira; Aparna Bharati; Joel Brogan; Kevin Bowyer; Patrick Flynn; Walter Scheirer; Anderson Rocha
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2072
ER -
Allan Pinto, Daniel Moreira, Aparna Bharati, Joel Brogan, Kevin Bowyer, Patrick Flynn, Walter Scheirer, Anderson Rocha. (2017). Provenance Filtering for Multimedia Phylogeny. IEEE SigPort. http://sigport.org/2072
Allan Pinto, Daniel Moreira, Aparna Bharati, Joel Brogan, Kevin Bowyer, Patrick Flynn, Walter Scheirer, Anderson Rocha, 2017. Provenance Filtering for Multimedia Phylogeny. Available at: http://sigport.org/2072.
Allan Pinto, Daniel Moreira, Aparna Bharati, Joel Brogan, Kevin Bowyer, Patrick Flynn, Walter Scheirer, Anderson Rocha. (2017). "Provenance Filtering for Multimedia Phylogeny." Web.
1. Allan Pinto, Daniel Moreira, Aparna Bharati, Joel Brogan, Kevin Bowyer, Patrick Flynn, Walter Scheirer, Anderson Rocha. Provenance Filtering for Multimedia Phylogeny [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2072

PROVENANCE FILTERING FOR MULTIMEDIA PHYLOGENY


Departing from traditional digital forensics modeling, which seeks to analyze single objects in isolation, multimedia phylogeny analyzes the evolutionary processes that influence digital objects and collections over time. One of its integral pieces is provenance filtering, which consists of searching a potentially large pool of objects for the most related ones with respect to a given query, in terms of possible ancestors (donors or contributors) and descendants.

Paper Details

Authors:
Allan Pinto, Daniel Moreira, Aparna Bharati, Joel Brogan, Kevin Bowyer, Patrick Flynn, Walter Scheirer, Anderson Rocha
Submitted On:
14 September 2017 - 10:39pm
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ICIP17_Prov_Filtering_Presentation.pptx

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[1] Allan Pinto, Daniel Moreira, Aparna Bharati, Joel Brogan, Kevin Bowyer, Patrick Flynn, Walter Scheirer, Anderson Rocha, "PROVENANCE FILTERING FOR MULTIMEDIA PHYLOGENY", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2071. Accessed: Sep. 20, 2020.
@article{2071-17,
url = {http://sigport.org/2071},
author = {Allan Pinto; Daniel Moreira; Aparna Bharati; Joel Brogan; Kevin Bowyer; Patrick Flynn; Walter Scheirer; Anderson Rocha },
publisher = {IEEE SigPort},
title = {PROVENANCE FILTERING FOR MULTIMEDIA PHYLOGENY},
year = {2017} }
TY - EJOUR
T1 - PROVENANCE FILTERING FOR MULTIMEDIA PHYLOGENY
AU - Allan Pinto; Daniel Moreira; Aparna Bharati; Joel Brogan; Kevin Bowyer; Patrick Flynn; Walter Scheirer; Anderson Rocha
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2071
ER -
Allan Pinto, Daniel Moreira, Aparna Bharati, Joel Brogan, Kevin Bowyer, Patrick Flynn, Walter Scheirer, Anderson Rocha. (2017). PROVENANCE FILTERING FOR MULTIMEDIA PHYLOGENY. IEEE SigPort. http://sigport.org/2071
Allan Pinto, Daniel Moreira, Aparna Bharati, Joel Brogan, Kevin Bowyer, Patrick Flynn, Walter Scheirer, Anderson Rocha, 2017. PROVENANCE FILTERING FOR MULTIMEDIA PHYLOGENY. Available at: http://sigport.org/2071.
Allan Pinto, Daniel Moreira, Aparna Bharati, Joel Brogan, Kevin Bowyer, Patrick Flynn, Walter Scheirer, Anderson Rocha. (2017). "PROVENANCE FILTERING FOR MULTIMEDIA PHYLOGENY." Web.
1. Allan Pinto, Daniel Moreira, Aparna Bharati, Joel Brogan, Kevin Bowyer, Patrick Flynn, Walter Scheirer, Anderson Rocha. PROVENANCE FILTERING FOR MULTIMEDIA PHYLOGENY [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2071

Unsupervised Deep Hashing with Stacked Convolutional Autoencoders

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Authors:
Bruno Crémilleux, Frédéric Jurie
Submitted On:
14 September 2017 - 6:42pm
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[1] Bruno Crémilleux, Frédéric Jurie, "Unsupervised Deep Hashing with Stacked Convolutional Autoencoders", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2053. Accessed: Sep. 20, 2020.
@article{2053-17,
url = {http://sigport.org/2053},
author = {Bruno Crémilleux; Frédéric Jurie },
publisher = {IEEE SigPort},
title = {Unsupervised Deep Hashing with Stacked Convolutional Autoencoders},
year = {2017} }
TY - EJOUR
T1 - Unsupervised Deep Hashing with Stacked Convolutional Autoencoders
AU - Bruno Crémilleux; Frédéric Jurie
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2053
ER -
Bruno Crémilleux, Frédéric Jurie. (2017). Unsupervised Deep Hashing with Stacked Convolutional Autoencoders. IEEE SigPort. http://sigport.org/2053
Bruno Crémilleux, Frédéric Jurie, 2017. Unsupervised Deep Hashing with Stacked Convolutional Autoencoders. Available at: http://sigport.org/2053.
Bruno Crémilleux, Frédéric Jurie. (2017). "Unsupervised Deep Hashing with Stacked Convolutional Autoencoders." Web.
1. Bruno Crémilleux, Frédéric Jurie. Unsupervised Deep Hashing with Stacked Convolutional Autoencoders [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2053

PERSON RE-IDENTIFICATION USING VISUAL ATTENTION


Despite recent attempts for solving the person re-identification problem, it remains a challenging task since a person’s appearance can vary significantly when large variations in view angle, human pose and illumination are involved. The concept of attention is one of the most interesting recent architectural innovations in neural networks. Inspired by that, in this paper we propose a novel approach based on using a gradient-based attention mechanism in deep convolution neural network for solving the person re-identification problem.

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Authors:
Hairong Qi
Submitted On:
14 September 2017 - 4:12pm
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Poster

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[1] Hairong Qi, "PERSON RE-IDENTIFICATION USING VISUAL ATTENTION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2046. Accessed: Sep. 20, 2020.
@article{2046-17,
url = {http://sigport.org/2046},
author = {Hairong Qi },
publisher = {IEEE SigPort},
title = {PERSON RE-IDENTIFICATION USING VISUAL ATTENTION},
year = {2017} }
TY - EJOUR
T1 - PERSON RE-IDENTIFICATION USING VISUAL ATTENTION
AU - Hairong Qi
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2046
ER -
Hairong Qi. (2017). PERSON RE-IDENTIFICATION USING VISUAL ATTENTION. IEEE SigPort. http://sigport.org/2046
Hairong Qi, 2017. PERSON RE-IDENTIFICATION USING VISUAL ATTENTION. Available at: http://sigport.org/2046.
Hairong Qi. (2017). "PERSON RE-IDENTIFICATION USING VISUAL ATTENTION." Web.
1. Hairong Qi. PERSON RE-IDENTIFICATION USING VISUAL ATTENTION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2046

LEARNING SUPERVISED BINARY HASHING: OPTIMIZATION VS DIVERSITY


Binary hashing is a practical approach for fast, approximate retrieval in large image databases. The goal is to learn a hash function that maps images onto a binary vector such that Hamming distances approximate semantic similarities. The search is then fast by using hardware support for binary operations. Most hashing papers define a complicated objective function that couples the single-bit hash functions.

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Authors:
Ramin Raziperchikolaei, Miguel Carreira-Perpinan
Submitted On:
14 September 2017 - 3:11pm
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[1] Ramin Raziperchikolaei, Miguel Carreira-Perpinan, "LEARNING SUPERVISED BINARY HASHING: OPTIMIZATION VS DIVERSITY", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2045. Accessed: Sep. 20, 2020.
@article{2045-17,
url = {http://sigport.org/2045},
author = {Ramin Raziperchikolaei; Miguel Carreira-Perpinan },
publisher = {IEEE SigPort},
title = {LEARNING SUPERVISED BINARY HASHING: OPTIMIZATION VS DIVERSITY},
year = {2017} }
TY - EJOUR
T1 - LEARNING SUPERVISED BINARY HASHING: OPTIMIZATION VS DIVERSITY
AU - Ramin Raziperchikolaei; Miguel Carreira-Perpinan
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2045
ER -
Ramin Raziperchikolaei, Miguel Carreira-Perpinan. (2017). LEARNING SUPERVISED BINARY HASHING: OPTIMIZATION VS DIVERSITY. IEEE SigPort. http://sigport.org/2045
Ramin Raziperchikolaei, Miguel Carreira-Perpinan, 2017. LEARNING SUPERVISED BINARY HASHING: OPTIMIZATION VS DIVERSITY. Available at: http://sigport.org/2045.
Ramin Raziperchikolaei, Miguel Carreira-Perpinan. (2017). "LEARNING SUPERVISED BINARY HASHING: OPTIMIZATION VS DIVERSITY." Web.
1. Ramin Raziperchikolaei, Miguel Carreira-Perpinan. LEARNING SUPERVISED BINARY HASHING: OPTIMIZATION VS DIVERSITY [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2045

ENHANCING FEATURE DISCRIMINATION FOR UNSUPERVISED HASHING


We introduce a novel approach to improve unsupervised hashing. Specifically, we propose a very efficient embedding method: Gaussian Mixture Model embedding (Gemb). The proposed method, using Gaussian Mixture Model, embeds feature vector into a low-dimensional vector and, simultaneously, enhances the discriminative property of features before passing them into hashing. Our experiment shows that the proposed method boosts the hashing performance of many state-of-the-art, e.g.

Paper Details

Authors:
Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, Ngai-Man Cheung
Submitted On:
14 September 2017 - 11:24am
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Enhance feature discrimination for Unsupervised hashing - poster.pdf

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[1] Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, Ngai-Man Cheung, "ENHANCING FEATURE DISCRIMINATION FOR UNSUPERVISED HASHING", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2038. Accessed: Sep. 20, 2020.
@article{2038-17,
url = {http://sigport.org/2038},
author = {Tuan Hoang; Thanh-Toan Do; Dang-Khoa Le Tan; Ngai-Man Cheung },
publisher = {IEEE SigPort},
title = {ENHANCING FEATURE DISCRIMINATION FOR UNSUPERVISED HASHING},
year = {2017} }
TY - EJOUR
T1 - ENHANCING FEATURE DISCRIMINATION FOR UNSUPERVISED HASHING
AU - Tuan Hoang; Thanh-Toan Do; Dang-Khoa Le Tan; Ngai-Man Cheung
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2038
ER -
Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, Ngai-Man Cheung. (2017). ENHANCING FEATURE DISCRIMINATION FOR UNSUPERVISED HASHING. IEEE SigPort. http://sigport.org/2038
Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, Ngai-Man Cheung, 2017. ENHANCING FEATURE DISCRIMINATION FOR UNSUPERVISED HASHING. Available at: http://sigport.org/2038.
Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, Ngai-Man Cheung. (2017). "ENHANCING FEATURE DISCRIMINATION FOR UNSUPERVISED HASHING." Web.
1. Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, Ngai-Man Cheung. ENHANCING FEATURE DISCRIMINATION FOR UNSUPERVISED HASHING [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2038

Enhance feature discrimination for Unsupervised hashing


We introduce a novel approach to improve unsupervised hashing. Specifically, we propose a very efficient embedding method: Gaussian Mixture Model embedding (Gemb). The proposed method, using Gaussian Mixture Model, embeds feature vector into a low-dimensional vector and, simultaneously, enhances the discriminative property of features before passing them into hashing. Our experiment shows that the proposed method boosts the hashing performance of many state-of-the-art, e.g.

Paper Details

Authors:
Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, Ngai-Man Cheung
Submitted On:
14 September 2017 - 11:17am
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Enhance feature%0D%0Adiscrimination for Unsupervised hashing - poster.pdf

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[1] Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, Ngai-Man Cheung, "Enhance feature discrimination for Unsupervised hashing", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2037. Accessed: Sep. 20, 2020.
@article{2037-17,
url = {http://sigport.org/2037},
author = {Tuan Hoang; Thanh-Toan Do; Dang-Khoa Le Tan; Ngai-Man Cheung },
publisher = {IEEE SigPort},
title = {Enhance feature discrimination for Unsupervised hashing},
year = {2017} }
TY - EJOUR
T1 - Enhance feature discrimination for Unsupervised hashing
AU - Tuan Hoang; Thanh-Toan Do; Dang-Khoa Le Tan; Ngai-Man Cheung
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2037
ER -
Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, Ngai-Man Cheung. (2017). Enhance feature discrimination for Unsupervised hashing. IEEE SigPort. http://sigport.org/2037
Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, Ngai-Man Cheung, 2017. Enhance feature discrimination for Unsupervised hashing. Available at: http://sigport.org/2037.
Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, Ngai-Man Cheung. (2017). "Enhance feature discrimination for Unsupervised hashing." Web.
1. Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, Ngai-Man Cheung. Enhance feature discrimination for Unsupervised hashing [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2037

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