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MMSP 2019

MMSP 2019 is the IEEE 21st International Workshop on Multimedia Signal Processing. The workshop is organized by the Multimedia Signal Processing Technical Committee (MMSP TC) of the IEEE Signal Processing Society (SPS). The workshop will bring together researcher and developers from different fields working on multimedia signal processing to share their experience, exchange ideas, explore future research directions and network.

Super-resolution of Omnidirectional Images Using Adversarial Learning


An omnidirectional image (ODI) enables viewers to look in every direction from a fixed point through a head-mounted display providing an immersive experience compared to that of a standard image. Designing immersive virtual reality systems with ODIs is challenging as they require high resolution content. In this paper, we study super-resolution for ODIs and propose an improved generative adversarial network based model which is optimized to handle the artifacts obtained in the spherical observational space.

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Authors:
Aakanksha Rana, Aljosa Smolic
Submitted On:
30 September 2019 - 3:45am
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[1] Aakanksha Rana, Aljosa Smolic, "Super-resolution of Omnidirectional Images Using Adversarial Learning ", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4849. Accessed: Oct. 18, 2019.
@article{4849-19,
url = {http://sigport.org/4849},
author = {Aakanksha Rana; Aljosa Smolic },
publisher = {IEEE SigPort},
title = {Super-resolution of Omnidirectional Images Using Adversarial Learning },
year = {2019} }
TY - EJOUR
T1 - Super-resolution of Omnidirectional Images Using Adversarial Learning
AU - Aakanksha Rana; Aljosa Smolic
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4849
ER -
Aakanksha Rana, Aljosa Smolic. (2019). Super-resolution of Omnidirectional Images Using Adversarial Learning . IEEE SigPort. http://sigport.org/4849
Aakanksha Rana, Aljosa Smolic, 2019. Super-resolution of Omnidirectional Images Using Adversarial Learning . Available at: http://sigport.org/4849.
Aakanksha Rana, Aljosa Smolic. (2019). "Super-resolution of Omnidirectional Images Using Adversarial Learning ." Web.
1. Aakanksha Rana, Aljosa Smolic. Super-resolution of Omnidirectional Images Using Adversarial Learning [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4849

Deep Metric Learning using Similarities from 
 Nonlinear Rank Approximations


In recent years, deep metric learning has achieved promising results in learning high dimensional semantic feature embeddings where the spatial relationships of the feature vectors match the visual similarities of the images. Similarity search for images is performed by determining the vectors with the smallest distances to a query vector. However, high retrieval quality does not depend on the actual distances of the feature vectors, but rather on the ranking order of the feature vectors from similar images.

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Authors:
Konstantin Schall, Kai Uwe Barthel, Nico Hezel, and Klaus Jung
Submitted On:
27 September 2019 - 10:06pm
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Nonlinear Rank Approximation Loss

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[1] Konstantin Schall, Kai Uwe Barthel, Nico Hezel, and Klaus Jung, "Deep Metric Learning using Similarities from 
 Nonlinear Rank Approximations", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4847. Accessed: Oct. 18, 2019.
@article{4847-19,
url = {http://sigport.org/4847},
author = {Konstantin Schall; Kai Uwe Barthel; Nico Hezel; and Klaus Jung },
publisher = {IEEE SigPort},
title = {Deep Metric Learning using Similarities from 
 Nonlinear Rank Approximations},
year = {2019} }
TY - EJOUR
T1 - Deep Metric Learning using Similarities from 
 Nonlinear Rank Approximations
AU - Konstantin Schall; Kai Uwe Barthel; Nico Hezel; and Klaus Jung
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4847
ER -
Konstantin Schall, Kai Uwe Barthel, Nico Hezel, and Klaus Jung. (2019). Deep Metric Learning using Similarities from 
 Nonlinear Rank Approximations. IEEE SigPort. http://sigport.org/4847
Konstantin Schall, Kai Uwe Barthel, Nico Hezel, and Klaus Jung, 2019. Deep Metric Learning using Similarities from 
 Nonlinear Rank Approximations. Available at: http://sigport.org/4847.
Konstantin Schall, Kai Uwe Barthel, Nico Hezel, and Klaus Jung. (2019). "Deep Metric Learning using Similarities from 
 Nonlinear Rank Approximations." Web.
1. Konstantin Schall, Kai Uwe Barthel, Nico Hezel, and Klaus Jung. Deep Metric Learning using Similarities from 
 Nonlinear Rank Approximations [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4847

3D Facial Expression Recognition Based on Multi-View and Prior Knowledge Fusion

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Submitted On:
27 September 2019 - 9:02am
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[1] , "3D Facial Expression Recognition Based on Multi-View and Prior Knowledge Fusion", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4846. Accessed: Oct. 18, 2019.
@article{4846-19,
url = {http://sigport.org/4846},
author = { },
publisher = {IEEE SigPort},
title = {3D Facial Expression Recognition Based on Multi-View and Prior Knowledge Fusion},
year = {2019} }
TY - EJOUR
T1 - 3D Facial Expression Recognition Based on Multi-View and Prior Knowledge Fusion
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4846
ER -
. (2019). 3D Facial Expression Recognition Based on Multi-View and Prior Knowledge Fusion. IEEE SigPort. http://sigport.org/4846
, 2019. 3D Facial Expression Recognition Based on Multi-View and Prior Knowledge Fusion. Available at: http://sigport.org/4846.
. (2019). "3D Facial Expression Recognition Based on Multi-View and Prior Knowledge Fusion." Web.
1. . 3D Facial Expression Recognition Based on Multi-View and Prior Knowledge Fusion [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4846

Selective Hearing: A Machine Listening Perspective


Selective hearing (SH) refers to the listeners' capability to focus their attention on a specific sound source or a group of sound sources in their auditory scene. This in turn implies that the listeners' focus is minimized for sources that are of no interest.
This paper describes the current landscape of machine listening research, and outlines ways in which these technologies can be leveraged to achieve SH with computational means.

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Authors:
Estefanía Cano, Hanna Lukashevich
Submitted On:
26 September 2019 - 8:41pm
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[1] Estefanía Cano, Hanna Lukashevich, "Selective Hearing: A Machine Listening Perspective", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4845. Accessed: Oct. 18, 2019.
@article{4845-19,
url = {http://sigport.org/4845},
author = {Estefanía Cano; Hanna Lukashevich },
publisher = {IEEE SigPort},
title = {Selective Hearing: A Machine Listening Perspective},
year = {2019} }
TY - EJOUR
T1 - Selective Hearing: A Machine Listening Perspective
AU - Estefanía Cano; Hanna Lukashevich
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4845
ER -
Estefanía Cano, Hanna Lukashevich. (2019). Selective Hearing: A Machine Listening Perspective. IEEE SigPort. http://sigport.org/4845
Estefanía Cano, Hanna Lukashevich, 2019. Selective Hearing: A Machine Listening Perspective. Available at: http://sigport.org/4845.
Estefanía Cano, Hanna Lukashevich. (2019). "Selective Hearing: A Machine Listening Perspective." Web.
1. Estefanía Cano, Hanna Lukashevich. Selective Hearing: A Machine Listening Perspective [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4845

Lowering Dynamic Power of a Stream-based CNN Hardware Accelerator


Custom hardware accelerators of Convolutional Neural Networks (CNN) provide a promising solution to meet real-time constraints for a wide range of applications on low-cost embedded devices. In this work, we aim to lower the dynamic power of a stream-based CNN hardware accelerator by reducing the computational redundancies in the CNN layers. In particular, we investigate the redundancies due to the downsampling effect of max pooling layers which are prevalent in state-of-the-art CNNs, and propose an approximation method to reduce the overall computations.

Paper Details

Authors:
Duvindu Piyasena, Rukshan Wickramasinghe, Debdeep Paul, Siew-Kei Lam, Meiqing Wu
Submitted On:
26 September 2019 - 8:30pm
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[1] Duvindu Piyasena, Rukshan Wickramasinghe, Debdeep Paul, Siew-Kei Lam, Meiqing Wu, "Lowering Dynamic Power of a Stream-based CNN Hardware Accelerator", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4844. Accessed: Oct. 18, 2019.
@article{4844-19,
url = {http://sigport.org/4844},
author = {Duvindu Piyasena; Rukshan Wickramasinghe; Debdeep Paul; Siew-Kei Lam; Meiqing Wu },
publisher = {IEEE SigPort},
title = {Lowering Dynamic Power of a Stream-based CNN Hardware Accelerator},
year = {2019} }
TY - EJOUR
T1 - Lowering Dynamic Power of a Stream-based CNN Hardware Accelerator
AU - Duvindu Piyasena; Rukshan Wickramasinghe; Debdeep Paul; Siew-Kei Lam; Meiqing Wu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4844
ER -
Duvindu Piyasena, Rukshan Wickramasinghe, Debdeep Paul, Siew-Kei Lam, Meiqing Wu. (2019). Lowering Dynamic Power of a Stream-based CNN Hardware Accelerator. IEEE SigPort. http://sigport.org/4844
Duvindu Piyasena, Rukshan Wickramasinghe, Debdeep Paul, Siew-Kei Lam, Meiqing Wu, 2019. Lowering Dynamic Power of a Stream-based CNN Hardware Accelerator. Available at: http://sigport.org/4844.
Duvindu Piyasena, Rukshan Wickramasinghe, Debdeep Paul, Siew-Kei Lam, Meiqing Wu. (2019). "Lowering Dynamic Power of a Stream-based CNN Hardware Accelerator." Web.
1. Duvindu Piyasena, Rukshan Wickramasinghe, Debdeep Paul, Siew-Kei Lam, Meiqing Wu. Lowering Dynamic Power of a Stream-based CNN Hardware Accelerator [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4844

An Efficient Logo Insertion Method for Video Coding in HEVC


Inserting a logo into HEVC video streams is highly demanded in video applications. In this paper, we present an efficient logo insertion method for video coding in HEVC. To reduce the impact of inserted logo, the proposed method mitigates the encoding dependence on logo by partitioning the video frame into separated regions. For lossless coding region, we reduce the bit rate overhead of lossless coding according to an error propagation model. For information reusing region, we partly re-encode the quality-loss area to maintain the encoding quality.

Paper Details

Authors:
Zhijie Huang, Jun Sun
Submitted On:
25 September 2019 - 8:41am
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[1] Zhijie Huang, Jun Sun, "An Efficient Logo Insertion Method for Video Coding in HEVC", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4841. Accessed: Oct. 18, 2019.
@article{4841-19,
url = {http://sigport.org/4841},
author = {Zhijie Huang; Jun Sun },
publisher = {IEEE SigPort},
title = {An Efficient Logo Insertion Method for Video Coding in HEVC},
year = {2019} }
TY - EJOUR
T1 - An Efficient Logo Insertion Method for Video Coding in HEVC
AU - Zhijie Huang; Jun Sun
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4841
ER -
Zhijie Huang, Jun Sun. (2019). An Efficient Logo Insertion Method for Video Coding in HEVC. IEEE SigPort. http://sigport.org/4841
Zhijie Huang, Jun Sun, 2019. An Efficient Logo Insertion Method for Video Coding in HEVC. Available at: http://sigport.org/4841.
Zhijie Huang, Jun Sun. (2019). "An Efficient Logo Insertion Method for Video Coding in HEVC." Web.
1. Zhijie Huang, Jun Sun. An Efficient Logo Insertion Method for Video Coding in HEVC [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4841

Deep Aggregation of Regional Convolutional Activations for Content Based Image Retrieval


One of the key challenges of deep learning based image retrieval remains in aggregating convolutional activations into one highly representative feature vector. Ideally, this descriptor should encode semantic, spatial and low level information. Even though off-the-shelf pre-trained neural networks can already produce good representations in combination with aggregation methods, appropriate fine tuning for the task of image retrieval has shown to significantly boost retrieval performance.

Paper Details

Authors:
Konstantin Schall, Kai Uwe Barthel, Nico Hezel, Klaus Jung
Submitted On:
25 September 2019 - 8:15am
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[1] Konstantin Schall, Kai Uwe Barthel, Nico Hezel, Klaus Jung, "Deep Aggregation of Regional Convolutional Activations for Content Based Image Retrieval", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4840. Accessed: Oct. 18, 2019.
@article{4840-19,
url = {http://sigport.org/4840},
author = {Konstantin Schall; Kai Uwe Barthel; Nico Hezel; Klaus Jung },
publisher = {IEEE SigPort},
title = {Deep Aggregation of Regional Convolutional Activations for Content Based Image Retrieval},
year = {2019} }
TY - EJOUR
T1 - Deep Aggregation of Regional Convolutional Activations for Content Based Image Retrieval
AU - Konstantin Schall; Kai Uwe Barthel; Nico Hezel; Klaus Jung
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4840
ER -
Konstantin Schall, Kai Uwe Barthel, Nico Hezel, Klaus Jung. (2019). Deep Aggregation of Regional Convolutional Activations for Content Based Image Retrieval. IEEE SigPort. http://sigport.org/4840
Konstantin Schall, Kai Uwe Barthel, Nico Hezel, Klaus Jung, 2019. Deep Aggregation of Regional Convolutional Activations for Content Based Image Retrieval. Available at: http://sigport.org/4840.
Konstantin Schall, Kai Uwe Barthel, Nico Hezel, Klaus Jung. (2019). "Deep Aggregation of Regional Convolutional Activations for Content Based Image Retrieval." Web.
1. Konstantin Schall, Kai Uwe Barthel, Nico Hezel, Klaus Jung. Deep Aggregation of Regional Convolutional Activations for Content Based Image Retrieval [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4840

Luminance-based Video Backdoor Attack Against Anti-spoofing Rebroadcast Detection

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Authors:
Abhir Bhalerao, Mauro Barni, Kassem Kallas, Benedetta Tondi
Submitted On:
25 September 2019 - 3:57am
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[1] Abhir Bhalerao, Mauro Barni, Kassem Kallas, Benedetta Tondi, "Luminance-based Video Backdoor Attack Against Anti-spoofing Rebroadcast Detection", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4839. Accessed: Oct. 18, 2019.
@article{4839-19,
url = {http://sigport.org/4839},
author = {Abhir Bhalerao; Mauro Barni; Kassem Kallas; Benedetta Tondi },
publisher = {IEEE SigPort},
title = {Luminance-based Video Backdoor Attack Against Anti-spoofing Rebroadcast Detection},
year = {2019} }
TY - EJOUR
T1 - Luminance-based Video Backdoor Attack Against Anti-spoofing Rebroadcast Detection
AU - Abhir Bhalerao; Mauro Barni; Kassem Kallas; Benedetta Tondi
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4839
ER -
Abhir Bhalerao, Mauro Barni, Kassem Kallas, Benedetta Tondi. (2019). Luminance-based Video Backdoor Attack Against Anti-spoofing Rebroadcast Detection. IEEE SigPort. http://sigport.org/4839
Abhir Bhalerao, Mauro Barni, Kassem Kallas, Benedetta Tondi, 2019. Luminance-based Video Backdoor Attack Against Anti-spoofing Rebroadcast Detection. Available at: http://sigport.org/4839.
Abhir Bhalerao, Mauro Barni, Kassem Kallas, Benedetta Tondi. (2019). "Luminance-based Video Backdoor Attack Against Anti-spoofing Rebroadcast Detection." Web.
1. Abhir Bhalerao, Mauro Barni, Kassem Kallas, Benedetta Tondi. Luminance-based Video Backdoor Attack Against Anti-spoofing Rebroadcast Detection [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4839

Lightweight Deep Convolutional Neural Networks for Facial Epression Recognition

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25 September 2019 - 12:50am
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[1] , "Lightweight Deep Convolutional Neural Networks for Facial Epression Recognition", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4838. Accessed: Oct. 18, 2019.
@article{4838-19,
url = {http://sigport.org/4838},
author = { },
publisher = {IEEE SigPort},
title = {Lightweight Deep Convolutional Neural Networks for Facial Epression Recognition},
year = {2019} }
TY - EJOUR
T1 - Lightweight Deep Convolutional Neural Networks for Facial Epression Recognition
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4838
ER -
. (2019). Lightweight Deep Convolutional Neural Networks for Facial Epression Recognition. IEEE SigPort. http://sigport.org/4838
, 2019. Lightweight Deep Convolutional Neural Networks for Facial Epression Recognition. Available at: http://sigport.org/4838.
. (2019). "Lightweight Deep Convolutional Neural Networks for Facial Epression Recognition." Web.
1. . Lightweight Deep Convolutional Neural Networks for Facial Epression Recognition [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4838

Multi-Label Classification for Automatic Human Blastocyst Grading with Severely Imbalanced Data


Quality scores assigned to blastocyst inner cell mass (ICM), trophectoderm (TE), and zona pellucida (ZP) are critical markers for predicting implantation potential of a human blastocyst in IVF treatment. Deep Convolutional Neural Networks (CNNs) have shown success in various image classification tasks, including classification of blastocysts into two quality categories. However, the problem of multi-label multi-class classification for blastocyst grading remains unsolved.

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Authors:
Parvaneh Saeedi, Jason Au, Jon Havelock
Submitted On:
24 September 2019 - 6:18pm
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[1] Parvaneh Saeedi, Jason Au, Jon Havelock, "Multi-Label Classification for Automatic Human Blastocyst Grading with Severely Imbalanced Data", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4837. Accessed: Oct. 18, 2019.
@article{4837-19,
url = {http://sigport.org/4837},
author = {Parvaneh Saeedi; Jason Au; Jon Havelock },
publisher = {IEEE SigPort},
title = {Multi-Label Classification for Automatic Human Blastocyst Grading with Severely Imbalanced Data},
year = {2019} }
TY - EJOUR
T1 - Multi-Label Classification for Automatic Human Blastocyst Grading with Severely Imbalanced Data
AU - Parvaneh Saeedi; Jason Au; Jon Havelock
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4837
ER -
Parvaneh Saeedi, Jason Au, Jon Havelock. (2019). Multi-Label Classification for Automatic Human Blastocyst Grading with Severely Imbalanced Data. IEEE SigPort. http://sigport.org/4837
Parvaneh Saeedi, Jason Au, Jon Havelock, 2019. Multi-Label Classification for Automatic Human Blastocyst Grading with Severely Imbalanced Data. Available at: http://sigport.org/4837.
Parvaneh Saeedi, Jason Au, Jon Havelock. (2019). "Multi-Label Classification for Automatic Human Blastocyst Grading with Severely Imbalanced Data." Web.
1. Parvaneh Saeedi, Jason Au, Jon Havelock. Multi-Label Classification for Automatic Human Blastocyst Grading with Severely Imbalanced Data [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4837

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