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Emerging: Big Data

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

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Authors:
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas
Submitted On:
1 March 2017 - 6:25am
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Summarization of Human Activity Videos Via Low-Rank Approximation

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[1] Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, "Summarization of Human Activity Videos Via Low-Rank Approximation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1545. Accessed: Dec. 17, 2017.
@article{1545-17,
url = {http://sigport.org/1545},
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/1545
ER -
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. (2017). Summarization of Human Activity Videos Via Low-Rank Approximation. IEEE SigPort. http://sigport.org/1545
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, 2017. Summarization of Human Activity Videos Via Low-Rank Approximation. Available at: http://sigport.org/1545.
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/1545

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

(356 downloads)

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[1] Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, "Summarization of Human Activity Videos Via Low-Rank Approximation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1544. Accessed: Dec. 17, 2017.
@article{1544-17,
url = {http://sigport.org/1544},
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/1544
ER -
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. (2017). Summarization of Human Activity Videos Via Low-Rank Approximation. IEEE SigPort. http://sigport.org/1544
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, 2017. Summarization of Human Activity Videos Via Low-Rank Approximation. Available at: http://sigport.org/1544.
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/1544

Fast and Stable Signal Deconvolution via Compressible State-Space Models


Objective: Common biological measurements are in
the form of noisy convolutions of signals of interest with possibly
unknown and transient blurring kernels. Examples include EEG
and calcium imaging data. Thus, signal deconvolution of these
measurements is crucial in understanding the underlying biological
processes. The objective of this paper is to develop fast and
stable solutions for signal deconvolution from noisy, blurred and
undersampled data, where the signals are in the form of discrete

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Authors:
Abbas Kazemipour, Ji Liu, Min Wu , Patrick Kanold and Behtash Babadi
Submitted On:
12 December 2016 - 9:35am
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FCSS_slides.pdf

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[1] Abbas Kazemipour, Ji Liu, Min Wu , Patrick Kanold and Behtash Babadi, "Fast and Stable Signal Deconvolution via Compressible State-Space Models", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1438. Accessed: Dec. 17, 2017.
@article{1438-16,
url = {http://sigport.org/1438},
author = {Abbas Kazemipour; Ji Liu; Min Wu ; Patrick Kanold and Behtash Babadi },
publisher = {IEEE SigPort},
title = {Fast and Stable Signal Deconvolution via Compressible State-Space Models},
year = {2016} }
TY - EJOUR
T1 - Fast and Stable Signal Deconvolution via Compressible State-Space Models
AU - Abbas Kazemipour; Ji Liu; Min Wu ; Patrick Kanold and Behtash Babadi
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1438
ER -
Abbas Kazemipour, Ji Liu, Min Wu , Patrick Kanold and Behtash Babadi. (2016). Fast and Stable Signal Deconvolution via Compressible State-Space Models. IEEE SigPort. http://sigport.org/1438
Abbas Kazemipour, Ji Liu, Min Wu , Patrick Kanold and Behtash Babadi, 2016. Fast and Stable Signal Deconvolution via Compressible State-Space Models. Available at: http://sigport.org/1438.
Abbas Kazemipour, Ji Liu, Min Wu , Patrick Kanold and Behtash Babadi. (2016). "Fast and Stable Signal Deconvolution via Compressible State-Space Models." Web.
1. Abbas Kazemipour, Ji Liu, Min Wu , Patrick Kanold and Behtash Babadi. Fast and Stable Signal Deconvolution via Compressible State-Space Models [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1438

Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems


Sampling and reconstruction of bandlimited graph signals have well-appreciated merits for dimensionality reduction, affordable storage, and online processing of streaming network data. However, these parsimonious signals are oftentimes encountered with high-dimensional linear inverse problems. Hence, interest shifts from reconstructing the signal itself towards instead approximating the input to a prescribed linear operator efficiently.

Paper Details

Authors:
Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro
Submitted On:
8 December 2016 - 12:32am
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sketching-globalsip16-presentation.pdf

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[1] Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro, "Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1420. Accessed: Dec. 17, 2017.
@article{1420-16,
url = {http://sigport.org/1420},
author = {Fernando Gama; Antonio G. Marques; Gonzalo Mateos; Alejandro Ribeiro },
publisher = {IEEE SigPort},
title = {Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems},
year = {2016} }
TY - EJOUR
T1 - Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems
AU - Fernando Gama; Antonio G. Marques; Gonzalo Mateos; Alejandro Ribeiro
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1420
ER -
Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro. (2016). Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems. IEEE SigPort. http://sigport.org/1420
Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro, 2016. Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems. Available at: http://sigport.org/1420.
Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro. (2016). "Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems." Web.
1. Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro. Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1420

Multilayer Spectral Graph Clustering via Convex Layer Aggregation


Multilayer graphs are commonly used for representing different relations between entities and handling heterogeneous data processing tasks. New challenges arise in multilayer graph clustering for assigning clusters to a common multilayer node set and for combining information from each layer. This paper presents a theoretical framework for multilayer spectral graph clustering of the nodes via convex layer aggregation.

Paper Details

Authors:
Pin-Yu Chen, Alfred Hero
Submitted On:
7 December 2016 - 10:03pm
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GlobalSIP_slides

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[1] Pin-Yu Chen, Alfred Hero, "Multilayer Spectral Graph Clustering via Convex Layer Aggregation", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1418. Accessed: Dec. 17, 2017.
@article{1418-16,
url = {http://sigport.org/1418},
author = {Pin-Yu Chen; Alfred Hero },
publisher = {IEEE SigPort},
title = {Multilayer Spectral Graph Clustering via Convex Layer Aggregation},
year = {2016} }
TY - EJOUR
T1 - Multilayer Spectral Graph Clustering via Convex Layer Aggregation
AU - Pin-Yu Chen; Alfred Hero
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1418
ER -
Pin-Yu Chen, Alfred Hero. (2016). Multilayer Spectral Graph Clustering via Convex Layer Aggregation. IEEE SigPort. http://sigport.org/1418
Pin-Yu Chen, Alfred Hero, 2016. Multilayer Spectral Graph Clustering via Convex Layer Aggregation. Available at: http://sigport.org/1418.
Pin-Yu Chen, Alfred Hero. (2016). "Multilayer Spectral Graph Clustering via Convex Layer Aggregation." Web.
1. Pin-Yu Chen, Alfred Hero. Multilayer Spectral Graph Clustering via Convex Layer Aggregation [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1418

Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems


Sampling and reconstruction of bandlimited graph signals have well-appreciated merits for dimensionality reduction, affordable storage, and online processing of streaming network data. However, these parsimonious signals are oftentimes encountered with high-dimensional linear inverse problems. Hence, interest shifts from reconstructing the signal itself towards instead approximating the input to a prescribed linear operator efficiently.

Paper Details

Authors:
Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro
Submitted On:
8 December 2016 - 3:51pm
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sketching-globalsip16-presentation.pdf

(178 downloads)

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[1] Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro, "Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1415. Accessed: Dec. 17, 2017.
@article{1415-16,
url = {http://sigport.org/1415},
author = {Fernando Gama; Antonio G. Marques; Gonzalo Mateos; Alejandro Ribeiro },
publisher = {IEEE SigPort},
title = {Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems},
year = {2016} }
TY - EJOUR
T1 - Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems
AU - Fernando Gama; Antonio G. Marques; Gonzalo Mateos; Alejandro Ribeiro
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1415
ER -
Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro. (2016). Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems. IEEE SigPort. http://sigport.org/1415
Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro, 2016. Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems. Available at: http://sigport.org/1415.
Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro. (2016). "Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems." Web.
1. Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro. Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1415

Scalable and Robust PCA Approach with Random Column/Row Sampling

Paper Details

Authors:
Mostafa Rahmani, George Atia
Submitted On:
7 December 2016 - 4:30pm
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Globalsip_RRD.pdf

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[1] Mostafa Rahmani, George Atia, "Scalable and Robust PCA Approach with Random Column/Row Sampling", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1414. Accessed: Dec. 17, 2017.
@article{1414-16,
url = {http://sigport.org/1414},
author = {Mostafa Rahmani; George Atia },
publisher = {IEEE SigPort},
title = {Scalable and Robust PCA Approach with Random Column/Row Sampling},
year = {2016} }
TY - EJOUR
T1 - Scalable and Robust PCA Approach with Random Column/Row Sampling
AU - Mostafa Rahmani; George Atia
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1414
ER -
Mostafa Rahmani, George Atia. (2016). Scalable and Robust PCA Approach with Random Column/Row Sampling. IEEE SigPort. http://sigport.org/1414
Mostafa Rahmani, George Atia, 2016. Scalable and Robust PCA Approach with Random Column/Row Sampling. Available at: http://sigport.org/1414.
Mostafa Rahmani, George Atia. (2016). "Scalable and Robust PCA Approach with Random Column/Row Sampling." Web.
1. Mostafa Rahmani, George Atia. Scalable and Robust PCA Approach with Random Column/Row Sampling [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1414

Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision


This work investigates the parameter estimation performance of super-resolution line spectral estimation using atomic norm minimization. The focus is on analyzing the algorithm's accuracy of inferring the frequencies and complex magnitudes from noisy observations. When the Signal-to-Noise Ratio is reasonably high and the true frequencies are separated by $O(\frac{1}{n})$, the atomic norm estimator is shown to localize the correct number of frequencies, each within a neighborhood of size $O(\sqrt{\frac{\log n}{n^3}} \sigma)$ of one of the true frequencies.

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Submitted On:
10 December 2016 - 3:39pm
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Slides_GlobalSIP.pdf

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[1] , "Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1383. Accessed: Dec. 17, 2017.
@article{1383-16,
url = {http://sigport.org/1383},
author = { },
publisher = {IEEE SigPort},
title = {Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision},
year = {2016} }
TY - EJOUR
T1 - Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1383
ER -
. (2016). Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision. IEEE SigPort. http://sigport.org/1383
, 2016. Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision. Available at: http://sigport.org/1383.
. (2016). "Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision." Web.
1. . Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1383

Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision


This work investigates the parameter estimation performance of super-resolution line spectral estimation using atomic norm minimization. The focus is on analyzing the algorithm's accuracy of inferring the frequencies and complex magnitudes from noisy observations. When the Signal-to-Noise Ratio is reasonably high and the true frequencies are separated by $O(\frac{1}{n})$, the atomic norm estimator is shown to localize the correct number of frequencies, each within a neighborhood of size $O(\sqrt{\frac{\log n}{n^3}} \sigma)$ of one of the true frequencies.

Paper Details

Authors:
Submitted On:
10 December 2016 - 3:38pm
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Slides_GlobalSIP.pdf

(160 downloads)

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[1] , "Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1382. Accessed: Dec. 17, 2017.
@article{1382-16,
url = {http://sigport.org/1382},
author = { },
publisher = {IEEE SigPort},
title = {Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision},
year = {2016} }
TY - EJOUR
T1 - Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1382
ER -
. (2016). Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision. IEEE SigPort. http://sigport.org/1382
, 2016. Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision. Available at: http://sigport.org/1382.
. (2016). "Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision." Web.
1. . Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1382

A Projection-free Decentralized Algorithm for Non-convex Optimization


This paper considers a decentralized projection free algorithm for non-convex optimization in high dimension. More specifically, we propose a Decentralized Frank-Wolfe (DeFW)
algorithm which is suitable when high dimensional optimization constraints are difficult to handle by conventional projection/proximal-based gradient descent methods. We present conditions under which the DeFW algorithm converges to a stationary point and prove that the rate of convergence is as fast as ${\cal O}( 1/\sqrt{T} )$, where

Paper Details

Authors:
Anna Scaglione, Jean Lafond, Eric Moulines
Submitted On:
7 December 2016 - 11:58pm
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ncvx_globalsip16.pdf

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[1] Anna Scaglione, Jean Lafond, Eric Moulines, "A Projection-free Decentralized Algorithm for Non-convex Optimization", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1381. Accessed: Dec. 17, 2017.
@article{1381-16,
url = {http://sigport.org/1381},
author = {Anna Scaglione; Jean Lafond; Eric Moulines },
publisher = {IEEE SigPort},
title = {A Projection-free Decentralized Algorithm for Non-convex Optimization},
year = {2016} }
TY - EJOUR
T1 - A Projection-free Decentralized Algorithm for Non-convex Optimization
AU - Anna Scaglione; Jean Lafond; Eric Moulines
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1381
ER -
Anna Scaglione, Jean Lafond, Eric Moulines. (2016). A Projection-free Decentralized Algorithm for Non-convex Optimization. IEEE SigPort. http://sigport.org/1381
Anna Scaglione, Jean Lafond, Eric Moulines, 2016. A Projection-free Decentralized Algorithm for Non-convex Optimization. Available at: http://sigport.org/1381.
Anna Scaglione, Jean Lafond, Eric Moulines. (2016). "A Projection-free Decentralized Algorithm for Non-convex Optimization." Web.
1. Anna Scaglione, Jean Lafond, Eric Moulines. A Projection-free Decentralized Algorithm for Non-convex Optimization [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1381

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