Sorry, you need to enable JavaScript to visit this website.

Image/Video Storage, Retrieval

A Pool of Deep Models for Event Recognition


This paper proposes a novel two-stage framework for event recognition in still images. First, for a generic event image, deep features, obtained via different pre-trained models, are fed into an ensemble of classifiers, whose posterior classification probabilities are thereafter fused by means of an order induced scheme, which penalizes the yielded scores according to their confidence in classifying the image at hand, and then averages them. Second, we combine the fusion results with a reverse matching paradigm in order to draw the final output of our proposed pipeline.

Paper Details

Authors:
Kashif Ahmad,Mohamed Lamine Mekhalfi, Nicola Conci, Giulia Boato, Farid Megani, Francesco De Natale
Submitted On:
12 September 2017 - 11:01am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICIP_2017_updated.pptx

(244)

Subscribe

[1] Kashif Ahmad,Mohamed Lamine Mekhalfi, Nicola Conci, Giulia Boato, Farid Megani, Francesco De Natale, "A Pool of Deep Models for Event Recognition", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1942. Accessed: Feb. 18, 2020.
@article{1942-17,
url = {http://sigport.org/1942},
author = {Kashif Ahmad;Mohamed Lamine Mekhalfi; Nicola Conci; Giulia Boato; Farid Megani; Francesco De Natale },
publisher = {IEEE SigPort},
title = {A Pool of Deep Models for Event Recognition},
year = {2017} }
TY - EJOUR
T1 - A Pool of Deep Models for Event Recognition
AU - Kashif Ahmad;Mohamed Lamine Mekhalfi; Nicola Conci; Giulia Boato; Farid Megani; Francesco De Natale
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1942
ER -
Kashif Ahmad,Mohamed Lamine Mekhalfi, Nicola Conci, Giulia Boato, Farid Megani, Francesco De Natale. (2017). A Pool of Deep Models for Event Recognition. IEEE SigPort. http://sigport.org/1942
Kashif Ahmad,Mohamed Lamine Mekhalfi, Nicola Conci, Giulia Boato, Farid Megani, Francesco De Natale, 2017. A Pool of Deep Models for Event Recognition. Available at: http://sigport.org/1942.
Kashif Ahmad,Mohamed Lamine Mekhalfi, Nicola Conci, Giulia Boato, Farid Megani, Francesco De Natale. (2017). "A Pool of Deep Models for Event Recognition." Web.
1. Kashif Ahmad,Mohamed Lamine Mekhalfi, Nicola Conci, Giulia Boato, Farid Megani, Francesco De Natale. A Pool of Deep Models for Event Recognition [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1942

A Pool of Deep Models for Event Recognition


This paper proposes a novel two-stage framework for event recognition in still images. First, for a generic event image, deep features, obtained via different pre-trained models, are fed into an ensemble of classifiers, whose posterior classification probabilities are thereafter fused by means of an order induced scheme, which penalizes the yielded scores according to their confidence in classifying the image at hand, and then averages them. Second, we combine the fusion results with a reverse matching paradigm in order to draw the final output of our proposed pipeline.

Paper Details

Authors:
Mohamed Lamine Mekhalfi, Nicola Conci, Giulia Boato, Farid Megani, Francesco De Natale
Submitted On:
12 September 2017 - 11:01am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICIP_2017_updated.pptx

(215)

Subscribe

[1] Mohamed Lamine Mekhalfi, Nicola Conci, Giulia Boato, Farid Megani, Francesco De Natale, "A Pool of Deep Models for Event Recognition", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1941. Accessed: Feb. 18, 2020.
@article{1941-17,
url = {http://sigport.org/1941},
author = {Mohamed Lamine Mekhalfi; Nicola Conci; Giulia Boato; Farid Megani; Francesco De Natale },
publisher = {IEEE SigPort},
title = {A Pool of Deep Models for Event Recognition},
year = {2017} }
TY - EJOUR
T1 - A Pool of Deep Models for Event Recognition
AU - Mohamed Lamine Mekhalfi; Nicola Conci; Giulia Boato; Farid Megani; Francesco De Natale
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1941
ER -
Mohamed Lamine Mekhalfi, Nicola Conci, Giulia Boato, Farid Megani, Francesco De Natale. (2017). A Pool of Deep Models for Event Recognition. IEEE SigPort. http://sigport.org/1941
Mohamed Lamine Mekhalfi, Nicola Conci, Giulia Boato, Farid Megani, Francesco De Natale, 2017. A Pool of Deep Models for Event Recognition. Available at: http://sigport.org/1941.
Mohamed Lamine Mekhalfi, Nicola Conci, Giulia Boato, Farid Megani, Francesco De Natale. (2017). "A Pool of Deep Models for Event Recognition." Web.
1. Mohamed Lamine Mekhalfi, Nicola Conci, Giulia Boato, Farid Megani, Francesco De Natale. A Pool of Deep Models for Event Recognition [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1941

Learning a Cross-Modal Hashing Network for Multimedia Search


In this paper, we propose a cross-modal hashing network (CMHN) method to learn compact binary codes for cross-modality multimedia search. Unlike most existing cross-modal hashing methods which learn a single pair of projections to map each example into a binary vector, we design a deep neural network to learn multiple pairs of hierarchical non-linear transformations, under which the nonlinear characteristics of samples can be well exploited and the modality gap is well reduced.

Paper Details

Authors:
Venice Erin Liong, Jiwen Lu, Yap-Peng Tan
Submitted On:
11 September 2017 - 5:44am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

icip2017_poster_2555.pdf

(234)

Subscribe

[1] Venice Erin Liong, Jiwen Lu, Yap-Peng Tan, "Learning a Cross-Modal Hashing Network for Multimedia Search", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1901. Accessed: Feb. 18, 2020.
@article{1901-17,
url = {http://sigport.org/1901},
author = {Venice Erin Liong; Jiwen Lu; Yap-Peng Tan },
publisher = {IEEE SigPort},
title = {Learning a Cross-Modal Hashing Network for Multimedia Search},
year = {2017} }
TY - EJOUR
T1 - Learning a Cross-Modal Hashing Network for Multimedia Search
AU - Venice Erin Liong; Jiwen Lu; Yap-Peng Tan
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1901
ER -
Venice Erin Liong, Jiwen Lu, Yap-Peng Tan. (2017). Learning a Cross-Modal Hashing Network for Multimedia Search. IEEE SigPort. http://sigport.org/1901
Venice Erin Liong, Jiwen Lu, Yap-Peng Tan, 2017. Learning a Cross-Modal Hashing Network for Multimedia Search. Available at: http://sigport.org/1901.
Venice Erin Liong, Jiwen Lu, Yap-Peng Tan. (2017). "Learning a Cross-Modal Hashing Network for Multimedia Search." Web.
1. Venice Erin Liong, Jiwen Lu, Yap-Peng Tan. Learning a Cross-Modal Hashing Network for Multimedia Search [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1901

SELF-PACED LEAST SQUARE SEMI-COUPLED DICTIONARY LEARNING FOR PERSON RE-IDENTIFICATION


Person re-identification aims to match people across disjoint camera views. It has been reported that Least Square
Semi-Coupled Dictionary Learning (LSSCDL) based samplespecific SVM learning framework has obtained the state of
the art performance. However, the objective function of the LSSCDL, the algorithm of learning the pairs (feature, weight)
dictionaries and the mapping function between feature space and weight space, is non-convex, which usually result in

Paper Details

Authors:
Wei Xu, Haoyuan Chi, Lei Zhou, Xiaolin Huang, Jie Yang
Submitted On:
7 September 2017 - 11:10am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

weixu_ICIP2017poster.pdf

(234)

Subscribe

[1] Wei Xu, Haoyuan Chi, Lei Zhou, Xiaolin Huang, Jie Yang, "SELF-PACED LEAST SQUARE SEMI-COUPLED DICTIONARY LEARNING FOR PERSON RE-IDENTIFICATION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1854. Accessed: Feb. 18, 2020.
@article{1854-17,
url = {http://sigport.org/1854},
author = {Wei Xu; Haoyuan Chi; Lei Zhou; Xiaolin Huang; Jie Yang },
publisher = {IEEE SigPort},
title = {SELF-PACED LEAST SQUARE SEMI-COUPLED DICTIONARY LEARNING FOR PERSON RE-IDENTIFICATION},
year = {2017} }
TY - EJOUR
T1 - SELF-PACED LEAST SQUARE SEMI-COUPLED DICTIONARY LEARNING FOR PERSON RE-IDENTIFICATION
AU - Wei Xu; Haoyuan Chi; Lei Zhou; Xiaolin Huang; Jie Yang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1854
ER -
Wei Xu, Haoyuan Chi, Lei Zhou, Xiaolin Huang, Jie Yang. (2017). SELF-PACED LEAST SQUARE SEMI-COUPLED DICTIONARY LEARNING FOR PERSON RE-IDENTIFICATION. IEEE SigPort. http://sigport.org/1854
Wei Xu, Haoyuan Chi, Lei Zhou, Xiaolin Huang, Jie Yang, 2017. SELF-PACED LEAST SQUARE SEMI-COUPLED DICTIONARY LEARNING FOR PERSON RE-IDENTIFICATION. Available at: http://sigport.org/1854.
Wei Xu, Haoyuan Chi, Lei Zhou, Xiaolin Huang, Jie Yang. (2017). "SELF-PACED LEAST SQUARE SEMI-COUPLED DICTIONARY LEARNING FOR PERSON RE-IDENTIFICATION." Web.
1. Wei Xu, Haoyuan Chi, Lei Zhou, Xiaolin Huang, Jie Yang. SELF-PACED LEAST SQUARE SEMI-COUPLED DICTIONARY LEARNING FOR PERSON RE-IDENTIFICATION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1854

LEVEL-SET FORMULATION BASED ON OTSU METHOD WITH MORPHOLOGICAL REGULARIZATION


Noisy image segmentation is one of the most important and challenging problem in computer vision. In this paper, we propose a level set segmentation technique inspired by the classic Otsu thresholding method. The front propagation of the proposed level set based method embeds a cost function that takes into account first-order statistical moments. In order to deal with highly noisy images, we also added a morphological step to our algorithm which led the final segmentation more robust and efficient.

Paper Details

Authors:
Alan M. Braga, Fátima N. S. de Medeiros, Regis C. P. Marques
Submitted On:
6 September 2017 - 10:35am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

poster-icip.pdf

(218)

Subscribe

[1] Alan M. Braga, Fátima N. S. de Medeiros, Regis C. P. Marques, "LEVEL-SET FORMULATION BASED ON OTSU METHOD WITH MORPHOLOGICAL REGULARIZATION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1844. Accessed: Feb. 18, 2020.
@article{1844-17,
url = {http://sigport.org/1844},
author = { Alan M. Braga; Fátima N. S. de Medeiros; Regis C. P. Marques },
publisher = {IEEE SigPort},
title = {LEVEL-SET FORMULATION BASED ON OTSU METHOD WITH MORPHOLOGICAL REGULARIZATION},
year = {2017} }
TY - EJOUR
T1 - LEVEL-SET FORMULATION BASED ON OTSU METHOD WITH MORPHOLOGICAL REGULARIZATION
AU - Alan M. Braga; Fátima N. S. de Medeiros; Regis C. P. Marques
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1844
ER -
Alan M. Braga, Fátima N. S. de Medeiros, Regis C. P. Marques. (2017). LEVEL-SET FORMULATION BASED ON OTSU METHOD WITH MORPHOLOGICAL REGULARIZATION. IEEE SigPort. http://sigport.org/1844
Alan M. Braga, Fátima N. S. de Medeiros, Regis C. P. Marques, 2017. LEVEL-SET FORMULATION BASED ON OTSU METHOD WITH MORPHOLOGICAL REGULARIZATION. Available at: http://sigport.org/1844.
Alan M. Braga, Fátima N. S. de Medeiros, Regis C. P. Marques. (2017). "LEVEL-SET FORMULATION BASED ON OTSU METHOD WITH MORPHOLOGICAL REGULARIZATION." Web.
1. Alan M. Braga, Fátima N. S. de Medeiros, Regis C. P. Marques. LEVEL-SET FORMULATION BASED ON OTSU METHOD WITH MORPHOLOGICAL REGULARIZATION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1844

Part Based Fine-grained Bird Image Retrieval Respecting Species Correlation

Paper Details

Authors:
Cheng Pang, Hongdong Li, Anoop Cherian, Hongxun Yao
Submitted On:
3 September 2017 - 3:43am
Short Link:
Type:
Event:
Paper Code:

Document Files

poster.pdf

(726)

Subscribe

[1] Cheng Pang, Hongdong Li, Anoop Cherian, Hongxun Yao, "Part Based Fine-grained Bird Image Retrieval Respecting Species Correlation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1821. Accessed: Feb. 18, 2020.
@article{1821-17,
url = {http://sigport.org/1821},
author = {Cheng Pang; Hongdong Li; Anoop Cherian; Hongxun Yao },
publisher = {IEEE SigPort},
title = {Part Based Fine-grained Bird Image Retrieval Respecting Species Correlation},
year = {2017} }
TY - EJOUR
T1 - Part Based Fine-grained Bird Image Retrieval Respecting Species Correlation
AU - Cheng Pang; Hongdong Li; Anoop Cherian; Hongxun Yao
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1821
ER -
Cheng Pang, Hongdong Li, Anoop Cherian, Hongxun Yao. (2017). Part Based Fine-grained Bird Image Retrieval Respecting Species Correlation. IEEE SigPort. http://sigport.org/1821
Cheng Pang, Hongdong Li, Anoop Cherian, Hongxun Yao, 2017. Part Based Fine-grained Bird Image Retrieval Respecting Species Correlation. Available at: http://sigport.org/1821.
Cheng Pang, Hongdong Li, Anoop Cherian, Hongxun Yao. (2017). "Part Based Fine-grained Bird Image Retrieval Respecting Species Correlation." Web.
1. Cheng Pang, Hongdong Li, Anoop Cherian, Hongxun Yao. Part Based Fine-grained Bird Image Retrieval Respecting Species Correlation [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1821

EXTRACTING KEY FRAMES FROM FIRST-PERSON VIDEOS IN THE COMMON SPACE OF MULTIPLE SENSORS


Selecting authentic scenes about activities of daily living (ADL) is useful to support our memory of everyday life. Key-frame extraction for first-person vision (FPV) videos is a core technology to realize such memory assistant. However, most existing key-frame extraction methods have mainly focused on stable scenes not related to ADL and only used visual signals of the image sequence even though the activities usually associate with our visual experience. To deal with dynamically changing scenes of FPV about daily activities, integrating motion and visual signals are essential.

Paper Details

Authors:
Yujie Li, Atsunori Kanemura, Hideki Asoh, Taiki Miyanishi, Motoaki Kawanabe
Submitted On:
1 September 2017 - 2:09am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

icip.pdf

(452)

Subscribe

[1] Yujie Li, Atsunori Kanemura, Hideki Asoh, Taiki Miyanishi, Motoaki Kawanabe, "EXTRACTING KEY FRAMES FROM FIRST-PERSON VIDEOS IN THE COMMON SPACE OF MULTIPLE SENSORS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1816. Accessed: Feb. 18, 2020.
@article{1816-17,
url = {http://sigport.org/1816},
author = {Yujie Li; Atsunori Kanemura; Hideki Asoh; Taiki Miyanishi; Motoaki Kawanabe },
publisher = {IEEE SigPort},
title = {EXTRACTING KEY FRAMES FROM FIRST-PERSON VIDEOS IN THE COMMON SPACE OF MULTIPLE SENSORS},
year = {2017} }
TY - EJOUR
T1 - EXTRACTING KEY FRAMES FROM FIRST-PERSON VIDEOS IN THE COMMON SPACE OF MULTIPLE SENSORS
AU - Yujie Li; Atsunori Kanemura; Hideki Asoh; Taiki Miyanishi; Motoaki Kawanabe
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1816
ER -
Yujie Li, Atsunori Kanemura, Hideki Asoh, Taiki Miyanishi, Motoaki Kawanabe. (2017). EXTRACTING KEY FRAMES FROM FIRST-PERSON VIDEOS IN THE COMMON SPACE OF MULTIPLE SENSORS. IEEE SigPort. http://sigport.org/1816
Yujie Li, Atsunori Kanemura, Hideki Asoh, Taiki Miyanishi, Motoaki Kawanabe, 2017. EXTRACTING KEY FRAMES FROM FIRST-PERSON VIDEOS IN THE COMMON SPACE OF MULTIPLE SENSORS. Available at: http://sigport.org/1816.
Yujie Li, Atsunori Kanemura, Hideki Asoh, Taiki Miyanishi, Motoaki Kawanabe. (2017). "EXTRACTING KEY FRAMES FROM FIRST-PERSON VIDEOS IN THE COMMON SPACE OF MULTIPLE SENSORS." Web.
1. Yujie Li, Atsunori Kanemura, Hideki Asoh, Taiki Miyanishi, Motoaki Kawanabe. EXTRACTING KEY FRAMES FROM FIRST-PERSON VIDEOS IN THE COMMON SPACE OF MULTIPLE SENSORS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1816

Image Deblurring in the presence of Salt-and-Pepper Noise


This work addresses image recovery problem in the presence of salt-and-pepper noise and image blur. The salt-and-pepper noise reviewed as the impulsive noise, in this paper, is modeled as a sparse signal because of its impulsiveness. To accurately reconstruct the clean image and the blur kernel, the framelet domains are exploited to sparsely represent the image and the blur kernel. From the reformulations conducted, a joint estimation is devised to simultaneously perform the image recovery, the salt-and-pepper noise suppression and the blur kernel estimation under a optimization framework.

Paper Details

Authors:
Liming Hou, Hongqing Liu, Zhen Luo, Yi Zhou and Trieu-Kien Truong
Submitted On:
22 August 2017 - 10:21pm
Short Link:
Type:
Event:
Document Year:
Cite

Document Files

icip2017poster.pdf

(219)

Subscribe

[1] Liming Hou, Hongqing Liu, Zhen Luo, Yi Zhou and Trieu-Kien Truong, "Image Deblurring in the presence of Salt-and-Pepper Noise", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1803. Accessed: Feb. 18, 2020.
@article{1803-17,
url = {http://sigport.org/1803},
author = {Liming Hou; Hongqing Liu; Zhen Luo; Yi Zhou and Trieu-Kien Truong },
publisher = {IEEE SigPort},
title = {Image Deblurring in the presence of Salt-and-Pepper Noise},
year = {2017} }
TY - EJOUR
T1 - Image Deblurring in the presence of Salt-and-Pepper Noise
AU - Liming Hou; Hongqing Liu; Zhen Luo; Yi Zhou and Trieu-Kien Truong
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1803
ER -
Liming Hou, Hongqing Liu, Zhen Luo, Yi Zhou and Trieu-Kien Truong. (2017). Image Deblurring in the presence of Salt-and-Pepper Noise. IEEE SigPort. http://sigport.org/1803
Liming Hou, Hongqing Liu, Zhen Luo, Yi Zhou and Trieu-Kien Truong, 2017. Image Deblurring in the presence of Salt-and-Pepper Noise. Available at: http://sigport.org/1803.
Liming Hou, Hongqing Liu, Zhen Luo, Yi Zhou and Trieu-Kien Truong. (2017). "Image Deblurring in the presence of Salt-and-Pepper Noise." Web.
1. Liming Hou, Hongqing Liu, Zhen Luo, Yi Zhou and Trieu-Kien Truong. Image Deblurring in the presence of Salt-and-Pepper Noise [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1803

How should we evaluate supervised hashing?

Paper Details

Authors:
Matthijs Douze, Nicolas Usunier, Hervé Jégou
Submitted On:
2 March 2017 - 3:08pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

poster_landscape.pdf

(512)

Subscribe

[1] Matthijs Douze, Nicolas Usunier, Hervé Jégou, "How should we evaluate supervised hashing?", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1595. Accessed: Feb. 18, 2020.
@article{1595-17,
url = {http://sigport.org/1595},
author = {Matthijs Douze; Nicolas Usunier; Hervé Jégou },
publisher = {IEEE SigPort},
title = {How should we evaluate supervised hashing?},
year = {2017} }
TY - EJOUR
T1 - How should we evaluate supervised hashing?
AU - Matthijs Douze; Nicolas Usunier; Hervé Jégou
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1595
ER -
Matthijs Douze, Nicolas Usunier, Hervé Jégou. (2017). How should we evaluate supervised hashing?. IEEE SigPort. http://sigport.org/1595
Matthijs Douze, Nicolas Usunier, Hervé Jégou, 2017. How should we evaluate supervised hashing?. Available at: http://sigport.org/1595.
Matthijs Douze, Nicolas Usunier, Hervé Jégou. (2017). "How should we evaluate supervised hashing?." Web.
1. Matthijs Douze, Nicolas Usunier, Hervé Jégou. How should we evaluate supervised hashing? [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1595

Summarization of Human Activity Videos Via Low-Rank Approximation


Summarization of videos depicting human activities is a timely problem with important applications, e.g., in the domains of surveillance or film/TV production, that steadily becomes more relevant. Research on video summarization has mainly relied on global clustering or local (frame-by-frame) saliency methods to provide automated algorithmic

Paper Details

Authors:
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas
Submitted On:
1 March 2017 - 6:25am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Summarization of Human Activity Videos Via Low-Rank Approximation

(868)

Keywords

Additional Categories

Subscribe

[1] Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, "Summarization of Human Activity Videos Via Low-Rank Approximation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1548. Accessed: Feb. 18, 2020.
@article{1548-17,
url = {http://sigport.org/1548},
author = {Anastasios Tefas; Nikos Nikolaidis; Ioannis Pitas },
publisher = {IEEE SigPort},
title = {Summarization of Human Activity Videos Via Low-Rank Approximation},
year = {2017} }
TY - EJOUR
T1 - Summarization of Human Activity Videos Via Low-Rank Approximation
AU - Anastasios Tefas; Nikos Nikolaidis; Ioannis Pitas
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1548
ER -
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. (2017). Summarization of Human Activity Videos Via Low-Rank Approximation. IEEE SigPort. http://sigport.org/1548
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, 2017. Summarization of Human Activity Videos Via Low-Rank Approximation. Available at: http://sigport.org/1548.
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. (2017). "Summarization of Human Activity Videos Via Low-Rank Approximation." Web.
1. Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. Summarization of Human Activity Videos Via Low-Rank Approximation [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1548

Pages