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Image/Video Storage, Retrieval

Deep Multi-Region Hashing

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15 May 2020 - 12:09am
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paper slide: Deep Multi-Region Hashing

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[1] , "Deep Multi-Region Hashing", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5332. Accessed: Jul. 04, 2020.
@article{5332-20,
url = {http://sigport.org/5332},
author = { },
publisher = {IEEE SigPort},
title = {Deep Multi-Region Hashing},
year = {2020} }
TY - EJOUR
T1 - Deep Multi-Region Hashing
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5332
ER -
. (2020). Deep Multi-Region Hashing. IEEE SigPort. http://sigport.org/5332
, 2020. Deep Multi-Region Hashing. Available at: http://sigport.org/5332.
. (2020). "Deep Multi-Region Hashing." Web.
1. . Deep Multi-Region Hashing [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5332

QUANTIZED TENSOR ROBUST PRINCIPAL COMPONENT ANALYSIS


High-dimensional data structures, known as tensors, are fundamental in many applications, including multispectral imaging and color video processing. Compression of such huge amount of multidimensional data collected over time is of paramount importance, necessitating the process of quantization of measurements into discrete values. Furthermore, noise and issues related to the acquisition and transmission of signals frequently lead to unobserved, lost or corrupted measurements.

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Authors:
Anastasia Aidini, Grigorios Tsagkatakis, Panagiotis Tsakalides
Submitted On:
14 May 2020 - 7:54am
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QTRPCA_presentation.pdf

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[1] Anastasia Aidini, Grigorios Tsagkatakis, Panagiotis Tsakalides, "QUANTIZED TENSOR ROBUST PRINCIPAL COMPONENT ANALYSIS", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5288. Accessed: Jul. 04, 2020.
@article{5288-20,
url = {http://sigport.org/5288},
author = {Anastasia Aidini; Grigorios Tsagkatakis; Panagiotis Tsakalides },
publisher = {IEEE SigPort},
title = {QUANTIZED TENSOR ROBUST PRINCIPAL COMPONENT ANALYSIS},
year = {2020} }
TY - EJOUR
T1 - QUANTIZED TENSOR ROBUST PRINCIPAL COMPONENT ANALYSIS
AU - Anastasia Aidini; Grigorios Tsagkatakis; Panagiotis Tsakalides
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5288
ER -
Anastasia Aidini, Grigorios Tsagkatakis, Panagiotis Tsakalides. (2020). QUANTIZED TENSOR ROBUST PRINCIPAL COMPONENT ANALYSIS. IEEE SigPort. http://sigport.org/5288
Anastasia Aidini, Grigorios Tsagkatakis, Panagiotis Tsakalides, 2020. QUANTIZED TENSOR ROBUST PRINCIPAL COMPONENT ANALYSIS. Available at: http://sigport.org/5288.
Anastasia Aidini, Grigorios Tsagkatakis, Panagiotis Tsakalides. (2020). "QUANTIZED TENSOR ROBUST PRINCIPAL COMPONENT ANALYSIS." Web.
1. Anastasia Aidini, Grigorios Tsagkatakis, Panagiotis Tsakalides. QUANTIZED TENSOR ROBUST PRINCIPAL COMPONENT ANALYSIS [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5288

Super-Resolution in Compressive Coded Imaging Systems via l2 − l1 − l2 Minimization Under a Deep Learning Approach


In most imaging applications the spatial resolution is a concern of the systems, but increasing the resolution of the sensor increases substantially the implementation cost. One option with lower cost is the use of spatial light modulators, which allows improving the reconstructed image resolution by including a high-resolution codification.

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Authors:
Hans Garcia, Miguel Marquez, Henry Arguello
Submitted On:
31 March 2020 - 4:47am
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[1] Hans Garcia, Miguel Marquez, Henry Arguello, "Super-Resolution in Compressive Coded Imaging Systems via l2 − l1 − l2 Minimization Under a Deep Learning Approach", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5075. Accessed: Jul. 04, 2020.
@article{5075-20,
url = {http://sigport.org/5075},
author = {Hans Garcia; Miguel Marquez; Henry Arguello },
publisher = {IEEE SigPort},
title = {Super-Resolution in Compressive Coded Imaging Systems via l2 − l1 − l2 Minimization Under a Deep Learning Approach},
year = {2020} }
TY - EJOUR
T1 - Super-Resolution in Compressive Coded Imaging Systems via l2 − l1 − l2 Minimization Under a Deep Learning Approach
AU - Hans Garcia; Miguel Marquez; Henry Arguello
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5075
ER -
Hans Garcia, Miguel Marquez, Henry Arguello. (2020). Super-Resolution in Compressive Coded Imaging Systems via l2 − l1 − l2 Minimization Under a Deep Learning Approach. IEEE SigPort. http://sigport.org/5075
Hans Garcia, Miguel Marquez, Henry Arguello, 2020. Super-Resolution in Compressive Coded Imaging Systems via l2 − l1 − l2 Minimization Under a Deep Learning Approach. Available at: http://sigport.org/5075.
Hans Garcia, Miguel Marquez, Henry Arguello. (2020). "Super-Resolution in Compressive Coded Imaging Systems via l2 − l1 − l2 Minimization Under a Deep Learning Approach." Web.
1. Hans Garcia, Miguel Marquez, Henry Arguello. Super-Resolution in Compressive Coded Imaging Systems via l2 − l1 − l2 Minimization Under a Deep Learning Approach [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5075

Tensor Dictionary Learning with representation quantization for Remote Sensing Observation Compression


Nowadays, multidimensional data structures, known as tensors, are widely used in many applications like earth observation from remote sensing image sequences. However, the increasing spatial, spectral and temporal resolution of the acquired images, introduces considerable challenges in terms of data storage and transfer, making critical the necessity of an efficient compression system for high dimensional data. In this paper, we propose a tensor-based compression algorithm that retains the structure of the data and achieves a high compression ratio.

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Authors:
Anastasia Aidini, Grigorios Tsagkatakis, and Panagiotis Tsakalides
Submitted On:
30 March 2020 - 8:25am
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dcc_aidini.pdf

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[1] Anastasia Aidini, Grigorios Tsagkatakis, and Panagiotis Tsakalides, "Tensor Dictionary Learning with representation quantization for Remote Sensing Observation Compression", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5057. Accessed: Jul. 04, 2020.
@article{5057-20,
url = {http://sigport.org/5057},
author = {Anastasia Aidini; Grigorios Tsagkatakis; and Panagiotis Tsakalides },
publisher = {IEEE SigPort},
title = {Tensor Dictionary Learning with representation quantization for Remote Sensing Observation Compression},
year = {2020} }
TY - EJOUR
T1 - Tensor Dictionary Learning with representation quantization for Remote Sensing Observation Compression
AU - Anastasia Aidini; Grigorios Tsagkatakis; and Panagiotis Tsakalides
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5057
ER -
Anastasia Aidini, Grigorios Tsagkatakis, and Panagiotis Tsakalides. (2020). Tensor Dictionary Learning with representation quantization for Remote Sensing Observation Compression. IEEE SigPort. http://sigport.org/5057
Anastasia Aidini, Grigorios Tsagkatakis, and Panagiotis Tsakalides, 2020. Tensor Dictionary Learning with representation quantization for Remote Sensing Observation Compression. Available at: http://sigport.org/5057.
Anastasia Aidini, Grigorios Tsagkatakis, and Panagiotis Tsakalides. (2020). "Tensor Dictionary Learning with representation quantization for Remote Sensing Observation Compression." Web.
1. Anastasia Aidini, Grigorios Tsagkatakis, and Panagiotis Tsakalides. Tensor Dictionary Learning with representation quantization for Remote Sensing Observation Compression [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5057

Spatial-Temporal Fusion Convolutional Neural Network for Compressed Video enhancement in HEVC

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Authors:
Xiaoyu Xu, Jian Qian, Li Yu, Hongkui Wang, Hao Tao, Shengju Yu
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29 March 2020 - 9:20am
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DCC-2020.ppt

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[1] Xiaoyu Xu, Jian Qian, Li Yu, Hongkui Wang, Hao Tao, Shengju Yu, "Spatial-Temporal Fusion Convolutional Neural Network for Compressed Video enhancement in HEVC", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5048. Accessed: Jul. 04, 2020.
@article{5048-20,
url = {http://sigport.org/5048},
author = {Xiaoyu Xu; Jian Qian; Li Yu; Hongkui Wang; Hao Tao; Shengju Yu },
publisher = {IEEE SigPort},
title = {Spatial-Temporal Fusion Convolutional Neural Network for Compressed Video enhancement in HEVC},
year = {2020} }
TY - EJOUR
T1 - Spatial-Temporal Fusion Convolutional Neural Network for Compressed Video enhancement in HEVC
AU - Xiaoyu Xu; Jian Qian; Li Yu; Hongkui Wang; Hao Tao; Shengju Yu
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5048
ER -
Xiaoyu Xu, Jian Qian, Li Yu, Hongkui Wang, Hao Tao, Shengju Yu. (2020). Spatial-Temporal Fusion Convolutional Neural Network for Compressed Video enhancement in HEVC. IEEE SigPort. http://sigport.org/5048
Xiaoyu Xu, Jian Qian, Li Yu, Hongkui Wang, Hao Tao, Shengju Yu, 2020. Spatial-Temporal Fusion Convolutional Neural Network for Compressed Video enhancement in HEVC. Available at: http://sigport.org/5048.
Xiaoyu Xu, Jian Qian, Li Yu, Hongkui Wang, Hao Tao, Shengju Yu. (2020). "Spatial-Temporal Fusion Convolutional Neural Network for Compressed Video enhancement in HEVC." Web.
1. Xiaoyu Xu, Jian Qian, Li Yu, Hongkui Wang, Hao Tao, Shengju Yu. Spatial-Temporal Fusion Convolutional Neural Network for Compressed Video enhancement in HEVC [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5048

Compressive Classification via Deep Learning using Single-pixel Measurements


Single-pixel camera (SPC) captures encoded projections of the scene in a unique detector such that the number of compressive projections is lower than the size of the image. Traditionally, classification is not performed in the compressive domain because it is necessary to recover the underlying image before to classification. Based on the success of Deep Learning (DL) in classification approaches, this paper proposes to classify images using compressive measurements of SPC.

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Authors:
Jorge Bacca, Nelson Diaz, Henry Arguello
Submitted On:
25 March 2020 - 3:14pm
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[1] Jorge Bacca, Nelson Diaz, Henry Arguello, "Compressive Classification via Deep Learning using Single-pixel Measurements", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5028. Accessed: Jul. 04, 2020.
@article{5028-20,
url = {http://sigport.org/5028},
author = {Jorge Bacca; Nelson Diaz; Henry Arguello },
publisher = {IEEE SigPort},
title = {Compressive Classification via Deep Learning using Single-pixel Measurements},
year = {2020} }
TY - EJOUR
T1 - Compressive Classification via Deep Learning using Single-pixel Measurements
AU - Jorge Bacca; Nelson Diaz; Henry Arguello
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5028
ER -
Jorge Bacca, Nelson Diaz, Henry Arguello. (2020). Compressive Classification via Deep Learning using Single-pixel Measurements. IEEE SigPort. http://sigport.org/5028
Jorge Bacca, Nelson Diaz, Henry Arguello, 2020. Compressive Classification via Deep Learning using Single-pixel Measurements. Available at: http://sigport.org/5028.
Jorge Bacca, Nelson Diaz, Henry Arguello. (2020). "Compressive Classification via Deep Learning using Single-pixel Measurements." Web.
1. Jorge Bacca, Nelson Diaz, Henry Arguello. Compressive Classification via Deep Learning using Single-pixel Measurements [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5028

GENERATIVE MODELS FOR LOW-RANK VIDEO REPRESENTATION AND RECONSTRUCTION FROM COMPRESSIVE MEASUREMENTS


Generative models have recently received considerable attention in the field of compressive sensing. If an image belongs to the range of a pretrained generative network, we can recover it from its compressive measurements by estimating the underlying compact latent code. In practice, all the pretrained generators have certain range beyond which they fail to generate reliably. Recent researches show that convolutional generative structures are biased to generate natural images.

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Authors:
M. Salman Asif
Submitted On:
4 December 2019 - 7:13am
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[1] M. Salman Asif, "GENERATIVE MODELS FOR LOW-RANK VIDEO REPRESENTATION AND RECONSTRUCTION FROM COMPRESSIVE MEASUREMENTS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4966. Accessed: Jul. 04, 2020.
@article{4966-19,
url = {http://sigport.org/4966},
author = {M. Salman Asif },
publisher = {IEEE SigPort},
title = {GENERATIVE MODELS FOR LOW-RANK VIDEO REPRESENTATION AND RECONSTRUCTION FROM COMPRESSIVE MEASUREMENTS},
year = {2019} }
TY - EJOUR
T1 - GENERATIVE MODELS FOR LOW-RANK VIDEO REPRESENTATION AND RECONSTRUCTION FROM COMPRESSIVE MEASUREMENTS
AU - M. Salman Asif
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4966
ER -
M. Salman Asif. (2019). GENERATIVE MODELS FOR LOW-RANK VIDEO REPRESENTATION AND RECONSTRUCTION FROM COMPRESSIVE MEASUREMENTS. IEEE SigPort. http://sigport.org/4966
M. Salman Asif, 2019. GENERATIVE MODELS FOR LOW-RANK VIDEO REPRESENTATION AND RECONSTRUCTION FROM COMPRESSIVE MEASUREMENTS. Available at: http://sigport.org/4966.
M. Salman Asif. (2019). "GENERATIVE MODELS FOR LOW-RANK VIDEO REPRESENTATION AND RECONSTRUCTION FROM COMPRESSIVE MEASUREMENTS." Web.
1. M. Salman Asif. GENERATIVE MODELS FOR LOW-RANK VIDEO REPRESENTATION AND RECONSTRUCTION FROM COMPRESSIVE MEASUREMENTS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4966

Learning Product Codebooks using Vector-Quantized Autoencoders for Image Retrieval

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Authors:
Markus Flierl
Submitted On:
12 November 2019 - 8:47am
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[1] Markus Flierl, "Learning Product Codebooks using Vector-Quantized Autoencoders for Image Retrieval", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4950. Accessed: Jul. 04, 2020.
@article{4950-19,
url = {http://sigport.org/4950},
author = {Markus Flierl },
publisher = {IEEE SigPort},
title = {Learning Product Codebooks using Vector-Quantized Autoencoders for Image Retrieval},
year = {2019} }
TY - EJOUR
T1 - Learning Product Codebooks using Vector-Quantized Autoencoders for Image Retrieval
AU - Markus Flierl
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4950
ER -
Markus Flierl. (2019). Learning Product Codebooks using Vector-Quantized Autoencoders for Image Retrieval. IEEE SigPort. http://sigport.org/4950
Markus Flierl, 2019. Learning Product Codebooks using Vector-Quantized Autoencoders for Image Retrieval. Available at: http://sigport.org/4950.
Markus Flierl. (2019). "Learning Product Codebooks using Vector-Quantized Autoencoders for Image Retrieval." Web.
1. Markus Flierl. Learning Product Codebooks using Vector-Quantized Autoencoders for Image Retrieval [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4950

3D Shape Retrieval Through Multilayer RBF Neural Network


3D object retrieval involves more efforts mainly because major computer vision features are designed for 2D images, which is rarely applicable for 3D models. In this paper, we propose to retrieve the 3D models based on the implicit parameters learned from the radial base functions that represent the 3D objects. The radial base functions are learned from the RBF neural network. As deep neural networks can represent the data that is not linearly separable, we apply multiple layers' neural network to train the radial base functions.

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Authors:
Yahong Han
Submitted On:
22 September 2019 - 12:37am
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[1] Yahong Han, "3D Shape Retrieval Through Multilayer RBF Neural Network", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4813. Accessed: Jul. 04, 2020.
@article{4813-19,
url = {http://sigport.org/4813},
author = {Yahong Han },
publisher = {IEEE SigPort},
title = {3D Shape Retrieval Through Multilayer RBF Neural Network},
year = {2019} }
TY - EJOUR
T1 - 3D Shape Retrieval Through Multilayer RBF Neural Network
AU - Yahong Han
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4813
ER -
Yahong Han. (2019). 3D Shape Retrieval Through Multilayer RBF Neural Network. IEEE SigPort. http://sigport.org/4813
Yahong Han, 2019. 3D Shape Retrieval Through Multilayer RBF Neural Network. Available at: http://sigport.org/4813.
Yahong Han. (2019). "3D Shape Retrieval Through Multilayer RBF Neural Network." Web.
1. Yahong Han. 3D Shape Retrieval Through Multilayer RBF Neural Network [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4813

Dual reverse attention networks for person re-identification


In this paper, we enhance feature representation ability of person re-identification (Re-ID) by learning invariances to hard examples. Unlike previous works of hard examples mining and generating in image level, we propose a dual reverse attention network (DRANet) to generate hard examples in the convolutional feature space. Specifically, we use a classification branch of attention mechanism to model that ‘what’ in channel and ‘where’ in spatial dimensions are informative in the feature maps.

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Authors:
Shuangwei Liu, Lin Qi, Yunzhou Zhang, Weidong Shi
Submitted On:
20 September 2019 - 11:04am
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[1] Shuangwei Liu, Lin Qi, Yunzhou Zhang, Weidong Shi, "Dual reverse attention networks for person re-identification", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4784. Accessed: Jul. 04, 2020.
@article{4784-19,
url = {http://sigport.org/4784},
author = {Shuangwei Liu; Lin Qi; Yunzhou Zhang; Weidong Shi },
publisher = {IEEE SigPort},
title = {Dual reverse attention networks for person re-identification},
year = {2019} }
TY - EJOUR
T1 - Dual reverse attention networks for person re-identification
AU - Shuangwei Liu; Lin Qi; Yunzhou Zhang; Weidong Shi
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4784
ER -
Shuangwei Liu, Lin Qi, Yunzhou Zhang, Weidong Shi. (2019). Dual reverse attention networks for person re-identification. IEEE SigPort. http://sigport.org/4784
Shuangwei Liu, Lin Qi, Yunzhou Zhang, Weidong Shi, 2019. Dual reverse attention networks for person re-identification. Available at: http://sigport.org/4784.
Shuangwei Liu, Lin Qi, Yunzhou Zhang, Weidong Shi. (2019). "Dual reverse attention networks for person re-identification." Web.
1. Shuangwei Liu, Lin Qi, Yunzhou Zhang, Weidong Shi. Dual reverse attention networks for person re-identification [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4784

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