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Image/Video Storage, Retrieval

Sub-diffraction Imaging using Fourier Ptychography and Structured Sparsity


We consider the problem of super-resolution for sub-diffraction imaging. We adapt conventional Fourier ptychographic approaches, for the case where the images to be acquired have an underlying structured sparsity. We propose some sub-sampling strategies which can be easily adapted to existing ptychographic setups. We then use a novel technique called CoPRAM with some modifications, to recover sparse (and block sparse) images from sub-sampled ptychographic measurements.

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Authors:
Gauri Jagatap, Zhengyu Chen, Chinmay Hegde, Namrata Vaswani
Submitted On:
30 April 2018 - 2:40pm
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slides-icassp18-nofigs.pdf

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[1] Gauri Jagatap, Zhengyu Chen, Chinmay Hegde, Namrata Vaswani, "Sub-diffraction Imaging using Fourier Ptychography and Structured Sparsity", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3195. Accessed: May. 24, 2018.
@article{3195-18,
url = {http://sigport.org/3195},
author = {Gauri Jagatap; Zhengyu Chen; Chinmay Hegde; Namrata Vaswani },
publisher = {IEEE SigPort},
title = {Sub-diffraction Imaging using Fourier Ptychography and Structured Sparsity},
year = {2018} }
TY - EJOUR
T1 - Sub-diffraction Imaging using Fourier Ptychography and Structured Sparsity
AU - Gauri Jagatap; Zhengyu Chen; Chinmay Hegde; Namrata Vaswani
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3195
ER -
Gauri Jagatap, Zhengyu Chen, Chinmay Hegde, Namrata Vaswani. (2018). Sub-diffraction Imaging using Fourier Ptychography and Structured Sparsity. IEEE SigPort. http://sigport.org/3195
Gauri Jagatap, Zhengyu Chen, Chinmay Hegde, Namrata Vaswani, 2018. Sub-diffraction Imaging using Fourier Ptychography and Structured Sparsity. Available at: http://sigport.org/3195.
Gauri Jagatap, Zhengyu Chen, Chinmay Hegde, Namrata Vaswani. (2018). "Sub-diffraction Imaging using Fourier Ptychography and Structured Sparsity." Web.
1. Gauri Jagatap, Zhengyu Chen, Chinmay Hegde, Namrata Vaswani. Sub-diffraction Imaging using Fourier Ptychography and Structured Sparsity [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3195

L0-REGULARIZED HYBRID GRADIENT SPARSITY PRIORS FOR ROBUST SINGLE-IMAGE BLIND DEBLURRING


Single-image blind deblurring is a challenging ill-posed in- verse problem which aims to estimate both blur kernel and latent sharp image from only one observation. This paper fo- cuses on first estimating the blur kernel alone and then restor- ing the latent image since it has been proven to be more feasi- ble to handle the ill-posed nature during blind deblurring. To estimate an accurate blur kernel, L0-norm of both first- and second-order image gradients is proposed to regularize the final estimation result.

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Authors:
Ryan Wen Liu, Wei Yin, Shengwu Xiong, Silong Peng
Submitted On:
19 April 2018 - 9:44pm
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ICASSP2018-LECTURE .pdf

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[1] Ryan Wen Liu, Wei Yin, Shengwu Xiong, Silong Peng, "L0-REGULARIZED HYBRID GRADIENT SPARSITY PRIORS FOR ROBUST SINGLE-IMAGE BLIND DEBLURRING ", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3052. Accessed: May. 24, 2018.
@article{3052-18,
url = {http://sigport.org/3052},
author = {Ryan Wen Liu; Wei Yin; Shengwu Xiong; Silong Peng },
publisher = {IEEE SigPort},
title = {L0-REGULARIZED HYBRID GRADIENT SPARSITY PRIORS FOR ROBUST SINGLE-IMAGE BLIND DEBLURRING },
year = {2018} }
TY - EJOUR
T1 - L0-REGULARIZED HYBRID GRADIENT SPARSITY PRIORS FOR ROBUST SINGLE-IMAGE BLIND DEBLURRING
AU - Ryan Wen Liu; Wei Yin; Shengwu Xiong; Silong Peng
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3052
ER -
Ryan Wen Liu, Wei Yin, Shengwu Xiong, Silong Peng. (2018). L0-REGULARIZED HYBRID GRADIENT SPARSITY PRIORS FOR ROBUST SINGLE-IMAGE BLIND DEBLURRING . IEEE SigPort. http://sigport.org/3052
Ryan Wen Liu, Wei Yin, Shengwu Xiong, Silong Peng, 2018. L0-REGULARIZED HYBRID GRADIENT SPARSITY PRIORS FOR ROBUST SINGLE-IMAGE BLIND DEBLURRING . Available at: http://sigport.org/3052.
Ryan Wen Liu, Wei Yin, Shengwu Xiong, Silong Peng. (2018). "L0-REGULARIZED HYBRID GRADIENT SPARSITY PRIORS FOR ROBUST SINGLE-IMAGE BLIND DEBLURRING ." Web.
1. Ryan Wen Liu, Wei Yin, Shengwu Xiong, Silong Peng. L0-REGULARIZED HYBRID GRADIENT SPARSITY PRIORS FOR ROBUST SINGLE-IMAGE BLIND DEBLURRING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3052

Regularized SVD-based Video Frame Saliency for Unsupervised Activity Video Summarization


Storage, browsing and analysis of human activity videos can be significantly facilitated by automated video summarization. Unsupervised key-frame extraction remains the most widely applicable technique for summarizing activity videos. However, their specific properties make the problem difficult to solve. Typical relevant algorithms fall under the video frame clustering or the dictionary-of-representatives families, with salient dictionary learning having been recently proposed.

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Authors:
Ioannis Mademlis, Anastasios Tefas, Ioannis Pitas
Submitted On:
18 April 2018 - 4:17am
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Regularized SVD-based Video Frame Saliency for Unsupervised Activity Video Summarization

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[1] Ioannis Mademlis, Anastasios Tefas, Ioannis Pitas, "Regularized SVD-based Video Frame Saliency for Unsupervised Activity Video Summarization", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2963. Accessed: May. 24, 2018.
@article{2963-18,
url = {http://sigport.org/2963},
author = {Ioannis Mademlis; Anastasios Tefas; Ioannis Pitas },
publisher = {IEEE SigPort},
title = {Regularized SVD-based Video Frame Saliency for Unsupervised Activity Video Summarization},
year = {2018} }
TY - EJOUR
T1 - Regularized SVD-based Video Frame Saliency for Unsupervised Activity Video Summarization
AU - Ioannis Mademlis; Anastasios Tefas; Ioannis Pitas
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2963
ER -
Ioannis Mademlis, Anastasios Tefas, Ioannis Pitas. (2018). Regularized SVD-based Video Frame Saliency for Unsupervised Activity Video Summarization. IEEE SigPort. http://sigport.org/2963
Ioannis Mademlis, Anastasios Tefas, Ioannis Pitas, 2018. Regularized SVD-based Video Frame Saliency for Unsupervised Activity Video Summarization. Available at: http://sigport.org/2963.
Ioannis Mademlis, Anastasios Tefas, Ioannis Pitas. (2018). "Regularized SVD-based Video Frame Saliency for Unsupervised Activity Video Summarization." Web.
1. Ioannis Mademlis, Anastasios Tefas, Ioannis Pitas. Regularized SVD-based Video Frame Saliency for Unsupervised Activity Video Summarization [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2963

Tracked Instance Search


In this work we propose tracking as a generic addition to the instance search task. From video data perspective, much information that can be used is not taken into account in the traditional instance search approach. This work aims to provide insights on exploiting such existing information by means of tracking and the proper combination of the results, independently of the instance search system. We also present a study on the improvement of the system when using multiple independent instances (up to 4) of the same person

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Authors:
Andreu Girbau, Ryota Hinami, Shin'ichi Satoh
Submitted On:
14 April 2018 - 4:04am
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[ICASSP 2018] Tracked Instance Search.pdf

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[1] Andreu Girbau, Ryota Hinami, Shin'ichi Satoh, "Tracked Instance Search ", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2811. Accessed: May. 24, 2018.
@article{2811-18,
url = {http://sigport.org/2811},
author = {Andreu Girbau; Ryota Hinami; Shin'ichi Satoh },
publisher = {IEEE SigPort},
title = {Tracked Instance Search },
year = {2018} }
TY - EJOUR
T1 - Tracked Instance Search
AU - Andreu Girbau; Ryota Hinami; Shin'ichi Satoh
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2811
ER -
Andreu Girbau, Ryota Hinami, Shin'ichi Satoh. (2018). Tracked Instance Search . IEEE SigPort. http://sigport.org/2811
Andreu Girbau, Ryota Hinami, Shin'ichi Satoh, 2018. Tracked Instance Search . Available at: http://sigport.org/2811.
Andreu Girbau, Ryota Hinami, Shin'ichi Satoh. (2018). "Tracked Instance Search ." Web.
1. Andreu Girbau, Ryota Hinami, Shin'ichi Satoh. Tracked Instance Search [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2811

APHASH ANCHOR-BASED PROBABILITY HASHING FOR IMAGE RETRIEVAL

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Authors:
Junjie Chen, Anran Wang, William K. Cheung
Submitted On:
13 April 2018 - 4:56am
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APHASH ANCHOR-BASED PROBABILITY HASHING FOR IMAGE RETRIEVAL.pdf

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[1] Junjie Chen, Anran Wang, William K. Cheung, "APHASH ANCHOR-BASED PROBABILITY HASHING FOR IMAGE RETRIEVAL", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2658. Accessed: May. 24, 2018.
@article{2658-18,
url = {http://sigport.org/2658},
author = {Junjie Chen; Anran Wang; William K. Cheung },
publisher = {IEEE SigPort},
title = {APHASH ANCHOR-BASED PROBABILITY HASHING FOR IMAGE RETRIEVAL},
year = {2018} }
TY - EJOUR
T1 - APHASH ANCHOR-BASED PROBABILITY HASHING FOR IMAGE RETRIEVAL
AU - Junjie Chen; Anran Wang; William K. Cheung
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2658
ER -
Junjie Chen, Anran Wang, William K. Cheung. (2018). APHASH ANCHOR-BASED PROBABILITY HASHING FOR IMAGE RETRIEVAL. IEEE SigPort. http://sigport.org/2658
Junjie Chen, Anran Wang, William K. Cheung, 2018. APHASH ANCHOR-BASED PROBABILITY HASHING FOR IMAGE RETRIEVAL. Available at: http://sigport.org/2658.
Junjie Chen, Anran Wang, William K. Cheung. (2018). "APHASH ANCHOR-BASED PROBABILITY HASHING FOR IMAGE RETRIEVAL." Web.
1. Junjie Chen, Anran Wang, William K. Cheung. APHASH ANCHOR-BASED PROBABILITY HASHING FOR IMAGE RETRIEVAL [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2658

QUERY EXPANSION WITH DIFFUSION ON MUTUAL RANK GRAPHS


In query expansion for object retrieval, there is substantial danger of query drift, where irrelevant information is inferred from pseudo-relevant images to enrich the query. To address this issue, we propose a query expansion method from the viewpoint of diffusion. It explores the structure of highly ranked images in a topological space, assuming that false positives reside on different manifolds from the query. For this purpose, a mutual rank graph is defined on pseudo-relevant images, and their distribution is learned by diffusing their query similarities through the graph.

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Authors:
Xiaomeng Wu, Go Irie, Kaoru Hiramatsu, and Kunio Kashino
Submitted On:
12 April 2018 - 10:03pm
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ICASSP18.2.pdf

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[1] Xiaomeng Wu, Go Irie, Kaoru Hiramatsu, and Kunio Kashino, "QUERY EXPANSION WITH DIFFUSION ON MUTUAL RANK GRAPHS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2554. Accessed: May. 24, 2018.
@article{2554-18,
url = {http://sigport.org/2554},
author = {Xiaomeng Wu; Go Irie; Kaoru Hiramatsu; and Kunio Kashino },
publisher = {IEEE SigPort},
title = {QUERY EXPANSION WITH DIFFUSION ON MUTUAL RANK GRAPHS},
year = {2018} }
TY - EJOUR
T1 - QUERY EXPANSION WITH DIFFUSION ON MUTUAL RANK GRAPHS
AU - Xiaomeng Wu; Go Irie; Kaoru Hiramatsu; and Kunio Kashino
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2554
ER -
Xiaomeng Wu, Go Irie, Kaoru Hiramatsu, and Kunio Kashino. (2018). QUERY EXPANSION WITH DIFFUSION ON MUTUAL RANK GRAPHS. IEEE SigPort. http://sigport.org/2554
Xiaomeng Wu, Go Irie, Kaoru Hiramatsu, and Kunio Kashino, 2018. QUERY EXPANSION WITH DIFFUSION ON MUTUAL RANK GRAPHS. Available at: http://sigport.org/2554.
Xiaomeng Wu, Go Irie, Kaoru Hiramatsu, and Kunio Kashino. (2018). "QUERY EXPANSION WITH DIFFUSION ON MUTUAL RANK GRAPHS." Web.
1. Xiaomeng Wu, Go Irie, Kaoru Hiramatsu, and Kunio Kashino. QUERY EXPANSION WITH DIFFUSION ON MUTUAL RANK GRAPHS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2554

A FEATURE EMBEDDING STRATEGY FOR HIGH-LEVEL CNN REPRESENTATIONS FROM MULTIPLE CONVNETS

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Authors:
Thangarajah Akilan, Jonathan Wu, Wei Jiang
Submitted On:
13 November 2017 - 9:13pm
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Transfer learning and feature embedding

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[1] Thangarajah Akilan, Jonathan Wu, Wei Jiang, " A FEATURE EMBEDDING STRATEGY FOR HIGH-LEVEL CNN REPRESENTATIONS FROM MULTIPLE CONVNETS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2345. Accessed: May. 24, 2018.
@article{2345-17,
url = {http://sigport.org/2345},
author = {Thangarajah Akilan; Jonathan Wu; Wei Jiang },
publisher = {IEEE SigPort},
title = { A FEATURE EMBEDDING STRATEGY FOR HIGH-LEVEL CNN REPRESENTATIONS FROM MULTIPLE CONVNETS},
year = {2017} }
TY - EJOUR
T1 - A FEATURE EMBEDDING STRATEGY FOR HIGH-LEVEL CNN REPRESENTATIONS FROM MULTIPLE CONVNETS
AU - Thangarajah Akilan; Jonathan Wu; Wei Jiang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2345
ER -
Thangarajah Akilan, Jonathan Wu, Wei Jiang. (2017). A FEATURE EMBEDDING STRATEGY FOR HIGH-LEVEL CNN REPRESENTATIONS FROM MULTIPLE CONVNETS. IEEE SigPort. http://sigport.org/2345
Thangarajah Akilan, Jonathan Wu, Wei Jiang, 2017. A FEATURE EMBEDDING STRATEGY FOR HIGH-LEVEL CNN REPRESENTATIONS FROM MULTIPLE CONVNETS. Available at: http://sigport.org/2345.
Thangarajah Akilan, Jonathan Wu, Wei Jiang. (2017). " A FEATURE EMBEDDING STRATEGY FOR HIGH-LEVEL CNN REPRESENTATIONS FROM MULTIPLE CONVNETS." Web.
1. Thangarajah Akilan, Jonathan Wu, Wei Jiang. A FEATURE EMBEDDING STRATEGY FOR HIGH-LEVEL CNN REPRESENTATIONS FROM MULTIPLE CONVNETS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2345

End-to-end Learning Binary Representation via Direct Binary Embedding


Learning binary representation is essential to large-scale computer vision tasks. Most existing algorithms require a separate quantization constraint to learn effective hashing functions. In this work, we present Direct Binary Embedding (DBE), a simple yet very effective algorithm to learn binary representation in an end-to-end fashion. By appending an ingeniously designed DBE layer to the deep convolutional neural network (DCNN), DBE learns binary code directly from the continuous DBE layer activation without quantization error.

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Authors:
Hairong Qi
Submitted On:
16 September 2017 - 12:01pm
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Liu_icip_17.pdf

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[1] Hairong Qi, "End-to-end Learning Binary Representation via Direct Binary Embedding", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2199. Accessed: May. 24, 2018.
@article{2199-17,
url = {http://sigport.org/2199},
author = {Hairong Qi },
publisher = {IEEE SigPort},
title = {End-to-end Learning Binary Representation via Direct Binary Embedding},
year = {2017} }
TY - EJOUR
T1 - End-to-end Learning Binary Representation via Direct Binary Embedding
AU - Hairong Qi
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2199
ER -
Hairong Qi. (2017). End-to-end Learning Binary Representation via Direct Binary Embedding. IEEE SigPort. http://sigport.org/2199
Hairong Qi, 2017. End-to-end Learning Binary Representation via Direct Binary Embedding. Available at: http://sigport.org/2199.
Hairong Qi. (2017). "End-to-end Learning Binary Representation via Direct Binary Embedding." Web.
1. Hairong Qi. End-to-end Learning Binary Representation via Direct Binary Embedding [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2199

Slides for ID 2913 at ICIP 2017

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Authors:
Junfu Pu, Yusuke Matsui, Fan Yang, Shin'ichi Satoh
Submitted On:
15 September 2017 - 8:28pm
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ICIP2017_ID2913.pdf

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[1] Junfu Pu, Yusuke Matsui, Fan Yang, Shin'ichi Satoh, "Slides for ID 2913 at ICIP 2017", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2163. Accessed: May. 24, 2018.
@article{2163-17,
url = {http://sigport.org/2163},
author = {Junfu Pu; Yusuke Matsui; Fan Yang; Shin'ichi Satoh },
publisher = {IEEE SigPort},
title = {Slides for ID 2913 at ICIP 2017},
year = {2017} }
TY - EJOUR
T1 - Slides for ID 2913 at ICIP 2017
AU - Junfu Pu; Yusuke Matsui; Fan Yang; Shin'ichi Satoh
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2163
ER -
Junfu Pu, Yusuke Matsui, Fan Yang, Shin'ichi Satoh. (2017). Slides for ID 2913 at ICIP 2017. IEEE SigPort. http://sigport.org/2163
Junfu Pu, Yusuke Matsui, Fan Yang, Shin'ichi Satoh, 2017. Slides for ID 2913 at ICIP 2017. Available at: http://sigport.org/2163.
Junfu Pu, Yusuke Matsui, Fan Yang, Shin'ichi Satoh. (2017). "Slides for ID 2913 at ICIP 2017." Web.
1. Junfu Pu, Yusuke Matsui, Fan Yang, Shin'ichi Satoh. Slides for ID 2913 at ICIP 2017 [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2163

LEARNING CIRCULANT SUPPORT VECTOR MACHINES FOR FAST IMAGE SEARCH


Binary hashing is an established approach for fast, approximate image search. It maps a query image to a binary vector so that Hamming distances approximate image similarities. Applying the hash function can be made fast by using a circulant matrix and the fast Fourier transform, but this circulant hash function must be learned optimally from training data. We show that a previously proposed learning algorithm based on optimization in the frequency domain is suboptimal.

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Authors:
Ramin Raziperchikolaei, Miguel Carreira-Perpinan
Submitted On:
15 September 2017 - 7:36pm
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icip17b-slides.pdf

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[1] Ramin Raziperchikolaei, Miguel Carreira-Perpinan, "LEARNING CIRCULANT SUPPORT VECTOR MACHINES FOR FAST IMAGE SEARCH", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2162. Accessed: May. 24, 2018.
@article{2162-17,
url = {http://sigport.org/2162},
author = {Ramin Raziperchikolaei; Miguel Carreira-Perpinan },
publisher = {IEEE SigPort},
title = {LEARNING CIRCULANT SUPPORT VECTOR MACHINES FOR FAST IMAGE SEARCH},
year = {2017} }
TY - EJOUR
T1 - LEARNING CIRCULANT SUPPORT VECTOR MACHINES FOR FAST IMAGE SEARCH
AU - Ramin Raziperchikolaei; Miguel Carreira-Perpinan
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2162
ER -
Ramin Raziperchikolaei, Miguel Carreira-Perpinan. (2017). LEARNING CIRCULANT SUPPORT VECTOR MACHINES FOR FAST IMAGE SEARCH. IEEE SigPort. http://sigport.org/2162
Ramin Raziperchikolaei, Miguel Carreira-Perpinan, 2017. LEARNING CIRCULANT SUPPORT VECTOR MACHINES FOR FAST IMAGE SEARCH. Available at: http://sigport.org/2162.
Ramin Raziperchikolaei, Miguel Carreira-Perpinan. (2017). "LEARNING CIRCULANT SUPPORT VECTOR MACHINES FOR FAST IMAGE SEARCH." Web.
1. Ramin Raziperchikolaei, Miguel Carreira-Perpinan. LEARNING CIRCULANT SUPPORT VECTOR MACHINES FOR FAST IMAGE SEARCH [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2162

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