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Multimedia Signal Processing

A HIERARCHICAL APPROACH TO EVENT DISCOVERY FROM SINGLE IMAGES USING MIL FRAMEWORK


In this poster, we propose to face the problem of event detection from single images, by exploiting both background information often containing revealing contextual clues and details, which are salient for recognizing the event. Such details are visual objects critical to understand the underlying event depicted in the image and were recently defined in the literature as ”event-saliency”. Adopting the Multiple-Instance Learning (MIL) paradigm we propose a hierarchical approach analyzing first the entire picture and then refining the decision on the basis of the event-salient objects.

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Authors:
Kashif Ahmad, Francesco De Natale, Giulia Boato, Andrea Rosani
Submitted On:
7 December 2016 - 10:30am
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GlobalSIP - A HIERARCHICAL APPROACH TO EVENT DISCOVERY FROM SINGLE IMAGES USING MIL FRAMEWORK.pdf

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[1] Kashif Ahmad, Francesco De Natale, Giulia Boato, Andrea Rosani, "A HIERARCHICAL APPROACH TO EVENT DISCOVERY FROM SINGLE IMAGES USING MIL FRAMEWORK", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1409. Accessed: Jun. 24, 2017.
@article{1409-16,
url = {http://sigport.org/1409},
author = {Kashif Ahmad; Francesco De Natale; Giulia Boato; Andrea Rosani },
publisher = {IEEE SigPort},
title = {A HIERARCHICAL APPROACH TO EVENT DISCOVERY FROM SINGLE IMAGES USING MIL FRAMEWORK},
year = {2016} }
TY - EJOUR
T1 - A HIERARCHICAL APPROACH TO EVENT DISCOVERY FROM SINGLE IMAGES USING MIL FRAMEWORK
AU - Kashif Ahmad; Francesco De Natale; Giulia Boato; Andrea Rosani
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1409
ER -
Kashif Ahmad, Francesco De Natale, Giulia Boato, Andrea Rosani. (2016). A HIERARCHICAL APPROACH TO EVENT DISCOVERY FROM SINGLE IMAGES USING MIL FRAMEWORK. IEEE SigPort. http://sigport.org/1409
Kashif Ahmad, Francesco De Natale, Giulia Boato, Andrea Rosani, 2016. A HIERARCHICAL APPROACH TO EVENT DISCOVERY FROM SINGLE IMAGES USING MIL FRAMEWORK. Available at: http://sigport.org/1409.
Kashif Ahmad, Francesco De Natale, Giulia Boato, Andrea Rosani. (2016). "A HIERARCHICAL APPROACH TO EVENT DISCOVERY FROM SINGLE IMAGES USING MIL FRAMEWORK." Web.
1. Kashif Ahmad, Francesco De Natale, Giulia Boato, Andrea Rosani. A HIERARCHICAL APPROACH TO EVENT DISCOVERY FROM SINGLE IMAGES USING MIL FRAMEWORK [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1409

TRACKING HIERARCHICAL STRUCTURE OF WEB VIDEO GROUPS BASED ON SALIENT KEYWORD MATCHING INCLUDING SEMANTIC BROADNESS ESTIMATION


This paper presents a novel method to track the hierarchical structure of Web video groups on the basis of salient keyword matching including semantic broadness estimation. To the best of our knowledge, this paper is the first work to perform extraction and tracking of the hierarchical structure simultaneously. Specifically, the proposed method first extracts the hierarchical structure of Web video groups and salient keywords of them on the basis of an improved scheme of our previously reported method.

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Authors:
Ryosuke Harakawa,Takahiro Ogawa,Miki Haseyama
Submitted On:
6 December 2016 - 6:46pm
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harakawa_globalsip2016_poster.pdf

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[1] Ryosuke Harakawa,Takahiro Ogawa,Miki Haseyama, "TRACKING HIERARCHICAL STRUCTURE OF WEB VIDEO GROUPS BASED ON SALIENT KEYWORD MATCHING INCLUDING SEMANTIC BROADNESS ESTIMATION", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1331. Accessed: Jun. 24, 2017.
@article{1331-16,
url = {http://sigport.org/1331},
author = {Ryosuke Harakawa;Takahiro Ogawa;Miki Haseyama },
publisher = {IEEE SigPort},
title = {TRACKING HIERARCHICAL STRUCTURE OF WEB VIDEO GROUPS BASED ON SALIENT KEYWORD MATCHING INCLUDING SEMANTIC BROADNESS ESTIMATION},
year = {2016} }
TY - EJOUR
T1 - TRACKING HIERARCHICAL STRUCTURE OF WEB VIDEO GROUPS BASED ON SALIENT KEYWORD MATCHING INCLUDING SEMANTIC BROADNESS ESTIMATION
AU - Ryosuke Harakawa;Takahiro Ogawa;Miki Haseyama
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1331
ER -
Ryosuke Harakawa,Takahiro Ogawa,Miki Haseyama. (2016). TRACKING HIERARCHICAL STRUCTURE OF WEB VIDEO GROUPS BASED ON SALIENT KEYWORD MATCHING INCLUDING SEMANTIC BROADNESS ESTIMATION. IEEE SigPort. http://sigport.org/1331
Ryosuke Harakawa,Takahiro Ogawa,Miki Haseyama, 2016. TRACKING HIERARCHICAL STRUCTURE OF WEB VIDEO GROUPS BASED ON SALIENT KEYWORD MATCHING INCLUDING SEMANTIC BROADNESS ESTIMATION. Available at: http://sigport.org/1331.
Ryosuke Harakawa,Takahiro Ogawa,Miki Haseyama. (2016). "TRACKING HIERARCHICAL STRUCTURE OF WEB VIDEO GROUPS BASED ON SALIENT KEYWORD MATCHING INCLUDING SEMANTIC BROADNESS ESTIMATION." Web.
1. Ryosuke Harakawa,Takahiro Ogawa,Miki Haseyama. TRACKING HIERARCHICAL STRUCTURE OF WEB VIDEO GROUPS BASED ON SALIENT KEYWORD MATCHING INCLUDING SEMANTIC BROADNESS ESTIMATION [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1331

A PARTIAL LEAST SQUARES BASED RANKER FOR FAST AND ACCURATE AGE ESTIMATION

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Authors:
HongLiu, Xiaohu Sun
Submitted On:
25 March 2016 - 12:35am
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pls_ranker_age_estimation_icassp2016_poster.pdf

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[1] HongLiu, Xiaohu Sun, "A PARTIAL LEAST SQUARES BASED RANKER FOR FAST AND ACCURATE AGE ESTIMATION", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1040. Accessed: Jun. 24, 2017.
@article{1040-16,
url = {http://sigport.org/1040},
author = {HongLiu; Xiaohu Sun },
publisher = {IEEE SigPort},
title = {A PARTIAL LEAST SQUARES BASED RANKER FOR FAST AND ACCURATE AGE ESTIMATION},
year = {2016} }
TY - EJOUR
T1 - A PARTIAL LEAST SQUARES BASED RANKER FOR FAST AND ACCURATE AGE ESTIMATION
AU - HongLiu; Xiaohu Sun
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1040
ER -
HongLiu, Xiaohu Sun. (2016). A PARTIAL LEAST SQUARES BASED RANKER FOR FAST AND ACCURATE AGE ESTIMATION. IEEE SigPort. http://sigport.org/1040
HongLiu, Xiaohu Sun, 2016. A PARTIAL LEAST SQUARES BASED RANKER FOR FAST AND ACCURATE AGE ESTIMATION. Available at: http://sigport.org/1040.
HongLiu, Xiaohu Sun. (2016). "A PARTIAL LEAST SQUARES BASED RANKER FOR FAST AND ACCURATE AGE ESTIMATION." Web.
1. HongLiu, Xiaohu Sun. A PARTIAL LEAST SQUARES BASED RANKER FOR FAST AND ACCURATE AGE ESTIMATION [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1040

LDADeep+: Latent Aspect Discovery with Deep Representations


LDADeep+ utilizes the high-level meaning of deep learning representation, and combines it with topic model to learn good aspects

Nowadays, with the success and fast growth of social media communities and mobile devices, people are encouraged to share their multimedia data online. Analyzing and summarizing data into useful information thus becomes increasingly important. For on- line photo sharing services like Flickr, when users are uploading a batch of daily photos at a time, the tags users provided tend to be rather vague, containing only a small amount of information.

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Authors:
Chieh-En Tsai, Hui-Lan Hsieh, Winston Hsu
Submitted On:
24 March 2016 - 12:09pm
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tsai_LDADeep_4_3 copy.pptx

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[1] Chieh-En Tsai, Hui-Lan Hsieh, Winston Hsu, "LDADeep+: Latent Aspect Discovery with Deep Representations", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1028. Accessed: Jun. 24, 2017.
@article{1028-16,
url = {http://sigport.org/1028},
author = {Chieh-En Tsai; Hui-Lan Hsieh; Winston Hsu },
publisher = {IEEE SigPort},
title = {LDADeep+: Latent Aspect Discovery with Deep Representations},
year = {2016} }
TY - EJOUR
T1 - LDADeep+: Latent Aspect Discovery with Deep Representations
AU - Chieh-En Tsai; Hui-Lan Hsieh; Winston Hsu
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1028
ER -
Chieh-En Tsai, Hui-Lan Hsieh, Winston Hsu. (2016). LDADeep+: Latent Aspect Discovery with Deep Representations. IEEE SigPort. http://sigport.org/1028
Chieh-En Tsai, Hui-Lan Hsieh, Winston Hsu, 2016. LDADeep+: Latent Aspect Discovery with Deep Representations. Available at: http://sigport.org/1028.
Chieh-En Tsai, Hui-Lan Hsieh, Winston Hsu. (2016). "LDADeep+: Latent Aspect Discovery with Deep Representations." Web.
1. Chieh-En Tsai, Hui-Lan Hsieh, Winston Hsu. LDADeep+: Latent Aspect Discovery with Deep Representations [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1028

Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics


Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics

In this paper, we present Discriminant Correlation Analysis (DCA), a feature level fusion technique that incorporates the class associations in correlation analysis of the feature sets. DCA performs an effective feature fusion by maximizing the pair-wise correlations across the two feature sets, and at the same time, eliminating the between-class correlations and restricting the correlations to be within classes.

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Authors:
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi
Submitted On:
16 July 2016 - 11:13pm
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DCA_ICASSP16_Poster.pdf

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[1] Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi, "Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/828. Accessed: Jun. 24, 2017.
@article{828-16,
url = {http://sigport.org/828},
author = {Mohammad Haghighat; Mohamed Abdel-Mottaleb; Wadee Alhalabi },
publisher = {IEEE SigPort},
title = {Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics},
year = {2016} }
TY - EJOUR
T1 - Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics
AU - Mohammad Haghighat; Mohamed Abdel-Mottaleb; Wadee Alhalabi
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/828
ER -
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi. (2016). Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics. IEEE SigPort. http://sigport.org/828
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi, 2016. Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics. Available at: http://sigport.org/828.
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi. (2016). "Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics." Web.
1. Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi. Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/828

Audio-Based Multimedia Event Detection Using Deep Recurrent Neural Networks


Multimedia event detection (MED) is the task of detecting given events (e.g. birthday party, making a sandwich) in a large collection of video clips. While visual features and automatic speech recognition typically provide the best features for this task, non-speech audio can also contribute useful information, such as crowds cheering, engine noises, or animal sounds.

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Authors:
Yun Wang, Leonardo Neves, Florian Metze
Submitted On:
17 March 2016 - 4:13pm
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2016.03 For ICASSP.ppt

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[1] Yun Wang, Leonardo Neves, Florian Metze, "Audio-Based Multimedia Event Detection Using Deep Recurrent Neural Networks", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/753. Accessed: Jun. 24, 2017.
@article{753-16,
url = {http://sigport.org/753},
author = {Yun Wang; Leonardo Neves; Florian Metze },
publisher = {IEEE SigPort},
title = {Audio-Based Multimedia Event Detection Using Deep Recurrent Neural Networks},
year = {2016} }
TY - EJOUR
T1 - Audio-Based Multimedia Event Detection Using Deep Recurrent Neural Networks
AU - Yun Wang; Leonardo Neves; Florian Metze
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/753
ER -
Yun Wang, Leonardo Neves, Florian Metze. (2016). Audio-Based Multimedia Event Detection Using Deep Recurrent Neural Networks. IEEE SigPort. http://sigport.org/753
Yun Wang, Leonardo Neves, Florian Metze, 2016. Audio-Based Multimedia Event Detection Using Deep Recurrent Neural Networks. Available at: http://sigport.org/753.
Yun Wang, Leonardo Neves, Florian Metze. (2016). "Audio-Based Multimedia Event Detection Using Deep Recurrent Neural Networks." Web.
1. Yun Wang, Leonardo Neves, Florian Metze. Audio-Based Multimedia Event Detection Using Deep Recurrent Neural Networks [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/753

Audio-Based Multimedia Event Detection Using Deep Recurrent Neural Networks


Multimedia event detection (MED) is the task of detecting given events (e.g. birthday party, making a sandwich) in a large collection of video clips. While visual features and automatic speech recognition typically provide the best features for this task, non-speech audio can also contribute useful information, such as crowds cheering, engine noises, or animal sounds.

Paper Details

Authors:
Yun Wang, Leonardo Neves, Florian Metze
Submitted On:
17 March 2016 - 4:13pm
Short Link:
Type:
Event:
Presenter's Name:
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Document Files

2016.03 For ICASSP.ppt

(190 downloads)

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[1] Yun Wang, Leonardo Neves, Florian Metze, "Audio-Based Multimedia Event Detection Using Deep Recurrent Neural Networks", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/751. Accessed: Jun. 24, 2017.
@article{751-16,
url = {http://sigport.org/751},
author = {Yun Wang; Leonardo Neves; Florian Metze },
publisher = {IEEE SigPort},
title = {Audio-Based Multimedia Event Detection Using Deep Recurrent Neural Networks},
year = {2016} }
TY - EJOUR
T1 - Audio-Based Multimedia Event Detection Using Deep Recurrent Neural Networks
AU - Yun Wang; Leonardo Neves; Florian Metze
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/751
ER -
Yun Wang, Leonardo Neves, Florian Metze. (2016). Audio-Based Multimedia Event Detection Using Deep Recurrent Neural Networks. IEEE SigPort. http://sigport.org/751
Yun Wang, Leonardo Neves, Florian Metze, 2016. Audio-Based Multimedia Event Detection Using Deep Recurrent Neural Networks. Available at: http://sigport.org/751.
Yun Wang, Leonardo Neves, Florian Metze. (2016). "Audio-Based Multimedia Event Detection Using Deep Recurrent Neural Networks." Web.
1. Yun Wang, Leonardo Neves, Florian Metze. Audio-Based Multimedia Event Detection Using Deep Recurrent Neural Networks [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/751

A Finite Field Cosine Transform-Based Image Processing Scheme for Color Image Encryption

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Authors:
Edmar S. da Silva, Ricardo M. Campello de Souza
Submitted On:
23 February 2016 - 1:44pm
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presentation.pdf

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[1] Edmar S. da Silva, Ricardo M. Campello de Souza, "A Finite Field Cosine Transform-Based Image Processing Scheme for Color Image Encryption", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/337. Accessed: Jun. 24, 2017.
@article{337-15,
url = {http://sigport.org/337},
author = {Edmar S. da Silva; Ricardo M. Campello de Souza },
publisher = {IEEE SigPort},
title = {A Finite Field Cosine Transform-Based Image Processing Scheme for Color Image Encryption},
year = {2015} }
TY - EJOUR
T1 - A Finite Field Cosine Transform-Based Image Processing Scheme for Color Image Encryption
AU - Edmar S. da Silva; Ricardo M. Campello de Souza
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/337
ER -
Edmar S. da Silva, Ricardo M. Campello de Souza. (2015). A Finite Field Cosine Transform-Based Image Processing Scheme for Color Image Encryption. IEEE SigPort. http://sigport.org/337
Edmar S. da Silva, Ricardo M. Campello de Souza, 2015. A Finite Field Cosine Transform-Based Image Processing Scheme for Color Image Encryption. Available at: http://sigport.org/337.
Edmar S. da Silva, Ricardo M. Campello de Souza. (2015). "A Finite Field Cosine Transform-Based Image Processing Scheme for Color Image Encryption." Web.
1. Edmar S. da Silva, Ricardo M. Campello de Souza. A Finite Field Cosine Transform-Based Image Processing Scheme for Color Image Encryption [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/337

Brief Introduction to Image Compression - Lecture Slides


Papers

Lecture notes @ Univ. Maryland
for ENEE408G Capstone Design: Multimedia Signal Processing

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Submitted On:
23 February 2016 - 1:43pm
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408S12_lec3_imgcode.pdf

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[1] , "Brief Introduction to Image Compression - Lecture Slides", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/134. Accessed: Jun. 24, 2017.
@article{134-15,
url = {http://sigport.org/134},
author = { },
publisher = {IEEE SigPort},
title = {Brief Introduction to Image Compression - Lecture Slides},
year = {2015} }
TY - EJOUR
T1 - Brief Introduction to Image Compression - Lecture Slides
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/134
ER -
. (2015). Brief Introduction to Image Compression - Lecture Slides. IEEE SigPort. http://sigport.org/134
, 2015. Brief Introduction to Image Compression - Lecture Slides. Available at: http://sigport.org/134.
. (2015). "Brief Introduction to Image Compression - Lecture Slides." Web.
1. . Brief Introduction to Image Compression - Lecture Slides [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/134