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Image/Video Storage, Retrieval

EDITED FILM ALIGNMENT VIA SELECTIVE HOUGH TRANSFORM AND ACCURATE TEMPLATE MATCHING


Edited film alignment is the post-production process of finding small parts of unedited footage that temporally and spatially match an edited film. The huge amount of data to be processed makes significant downsampling of the videos essential in real-life applications. Simultaneously, professional users demand that the task be achieved with frame and pixel-level accuracy. We propose a novel selective Hough transform (SHT) and an accurate template matching method to address the difficult trade-off between accuracy and scalability.

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Authors:
Xiaomeng Wu, Takahito Kawanishi, Minoru Mori, Kaoru Hiramatsu, and Kunio Kashino
Submitted On:
28 February 2017 - 11:28pm
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[1] Xiaomeng Wu, Takahito Kawanishi, Minoru Mori, Kaoru Hiramatsu, and Kunio Kashino, "EDITED FILM ALIGNMENT VIA SELECTIVE HOUGH TRANSFORM AND ACCURATE TEMPLATE MATCHING", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1528. Accessed: Sep. 20, 2020.
@article{1528-17,
url = {http://sigport.org/1528},
author = {Xiaomeng Wu; Takahito Kawanishi; Minoru Mori; Kaoru Hiramatsu; and Kunio Kashino },
publisher = {IEEE SigPort},
title = {EDITED FILM ALIGNMENT VIA SELECTIVE HOUGH TRANSFORM AND ACCURATE TEMPLATE MATCHING},
year = {2017} }
TY - EJOUR
T1 - EDITED FILM ALIGNMENT VIA SELECTIVE HOUGH TRANSFORM AND ACCURATE TEMPLATE MATCHING
AU - Xiaomeng Wu; Takahito Kawanishi; Minoru Mori; Kaoru Hiramatsu; and Kunio Kashino
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1528
ER -
Xiaomeng Wu, Takahito Kawanishi, Minoru Mori, Kaoru Hiramatsu, and Kunio Kashino. (2017). EDITED FILM ALIGNMENT VIA SELECTIVE HOUGH TRANSFORM AND ACCURATE TEMPLATE MATCHING. IEEE SigPort. http://sigport.org/1528
Xiaomeng Wu, Takahito Kawanishi, Minoru Mori, Kaoru Hiramatsu, and Kunio Kashino, 2017. EDITED FILM ALIGNMENT VIA SELECTIVE HOUGH TRANSFORM AND ACCURATE TEMPLATE MATCHING. Available at: http://sigport.org/1528.
Xiaomeng Wu, Takahito Kawanishi, Minoru Mori, Kaoru Hiramatsu, and Kunio Kashino. (2017). "EDITED FILM ALIGNMENT VIA SELECTIVE HOUGH TRANSFORM AND ACCURATE TEMPLATE MATCHING." Web.
1. Xiaomeng Wu, Takahito Kawanishi, Minoru Mori, Kaoru Hiramatsu, and Kunio Kashino. EDITED FILM ALIGNMENT VIA SELECTIVE HOUGH TRANSFORM AND ACCURATE TEMPLATE MATCHING [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1528

Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning

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28 February 2017 - 9:31pm
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[1] , "Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1520. Accessed: Sep. 20, 2020.
@article{1520-17,
url = {http://sigport.org/1520},
author = { },
publisher = {IEEE SigPort},
title = {Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning},
year = {2017} }
TY - EJOUR
T1 - Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1520
ER -
. (2017). Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning. IEEE SigPort. http://sigport.org/1520
, 2017. Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning. Available at: http://sigport.org/1520.
. (2017). "Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning." Web.
1. . Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1520

Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning

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28 February 2017 - 9:17pm
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[1] , "Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1519. Accessed: Sep. 20, 2020.
@article{1519-17,
url = {http://sigport.org/1519},
author = { },
publisher = {IEEE SigPort},
title = {Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning},
year = {2017} }
TY - EJOUR
T1 - Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1519
ER -
. (2017). Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning. IEEE SigPort. http://sigport.org/1519
, 2017. Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning. Available at: http://sigport.org/1519.
. (2017). "Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning." Web.
1. . Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1519

Enhanced Vote Count Circuit based on NOR Flash Memory for Fast Similarity Search

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Authors:
Haiyan Shu, Wenyu Jiang, Xiaoming Bao, Huan Zhou, Rongshan Yu
Submitted On:
20 March 2016 - 6:59am
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[1] Haiyan Shu, Wenyu Jiang, Xiaoming Bao, Huan Zhou, Rongshan Yu, "Enhanced Vote Count Circuit based on NOR Flash Memory for Fast Similarity Search", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/868. Accessed: Sep. 20, 2020.
@article{868-16,
url = {http://sigport.org/868},
author = {Haiyan Shu; Wenyu Jiang; Xiaoming Bao; Huan Zhou; Rongshan Yu },
publisher = {IEEE SigPort},
title = {Enhanced Vote Count Circuit based on NOR Flash Memory for Fast Similarity Search},
year = {2016} }
TY - EJOUR
T1 - Enhanced Vote Count Circuit based on NOR Flash Memory for Fast Similarity Search
AU - Haiyan Shu; Wenyu Jiang; Xiaoming Bao; Huan Zhou; Rongshan Yu
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/868
ER -
Haiyan Shu, Wenyu Jiang, Xiaoming Bao, Huan Zhou, Rongshan Yu. (2016). Enhanced Vote Count Circuit based on NOR Flash Memory for Fast Similarity Search. IEEE SigPort. http://sigport.org/868
Haiyan Shu, Wenyu Jiang, Xiaoming Bao, Huan Zhou, Rongshan Yu, 2016. Enhanced Vote Count Circuit based on NOR Flash Memory for Fast Similarity Search. Available at: http://sigport.org/868.
Haiyan Shu, Wenyu Jiang, Xiaoming Bao, Huan Zhou, Rongshan Yu. (2016). "Enhanced Vote Count Circuit based on NOR Flash Memory for Fast Similarity Search." Web.
1. Haiyan Shu, Wenyu Jiang, Xiaoming Bao, Huan Zhou, Rongshan Yu. Enhanced Vote Count Circuit based on NOR Flash Memory for Fast Similarity Search [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/868

Adaptive algorithms for hypergraph learning

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Authors:
Aikaterini Chasapi, Constantine Kotropoulos, Konstantinos Pliakos
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17 March 2016 - 7:18am
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[1] Aikaterini Chasapi, Constantine Kotropoulos, Konstantinos Pliakos, "Adaptive algorithms for hypergraph learning ", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/741. Accessed: Sep. 20, 2020.
@article{741-16,
url = {http://sigport.org/741},
author = {Aikaterini Chasapi; Constantine Kotropoulos; Konstantinos Pliakos },
publisher = {IEEE SigPort},
title = {Adaptive algorithms for hypergraph learning },
year = {2016} }
TY - EJOUR
T1 - Adaptive algorithms for hypergraph learning
AU - Aikaterini Chasapi; Constantine Kotropoulos; Konstantinos Pliakos
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/741
ER -
Aikaterini Chasapi, Constantine Kotropoulos, Konstantinos Pliakos. (2016). Adaptive algorithms for hypergraph learning . IEEE SigPort. http://sigport.org/741
Aikaterini Chasapi, Constantine Kotropoulos, Konstantinos Pliakos, 2016. Adaptive algorithms for hypergraph learning . Available at: http://sigport.org/741.
Aikaterini Chasapi, Constantine Kotropoulos, Konstantinos Pliakos. (2016). "Adaptive algorithms for hypergraph learning ." Web.
1. Aikaterini Chasapi, Constantine Kotropoulos, Konstantinos Pliakos. Adaptive algorithms for hypergraph learning [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/741

A Data Set Providing Synthetic and Real-World Fisheye Video Sequences


Synthetic fisheye image

In video surveillance as well as automotive applications, so-called fisheye cameras are often employed to capture a very wide angle of view. To be able to develop and evaluate algorithms specifically adapted to fisheye images and videos, a corresponding test data set is therefore introduced in this paper. The sequences are freely available via www.lms.lnt.de/fisheyedataset/.

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Authors:
Andrea Eichenseer, André Kaup
Submitted On:
16 March 2016 - 5:36am
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[1] Andrea Eichenseer, André Kaup, "A Data Set Providing Synthetic and Real-World Fisheye Video Sequences", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/709. Accessed: Sep. 20, 2020.
@article{709-16,
url = {http://sigport.org/709},
author = {Andrea Eichenseer; André Kaup },
publisher = {IEEE SigPort},
title = {A Data Set Providing Synthetic and Real-World Fisheye Video Sequences},
year = {2016} }
TY - EJOUR
T1 - A Data Set Providing Synthetic and Real-World Fisheye Video Sequences
AU - Andrea Eichenseer; André Kaup
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/709
ER -
Andrea Eichenseer, André Kaup. (2016). A Data Set Providing Synthetic and Real-World Fisheye Video Sequences. IEEE SigPort. http://sigport.org/709
Andrea Eichenseer, André Kaup, 2016. A Data Set Providing Synthetic and Real-World Fisheye Video Sequences. Available at: http://sigport.org/709.
Andrea Eichenseer, André Kaup. (2016). "A Data Set Providing Synthetic and Real-World Fisheye Video Sequences." Web.
1. Andrea Eichenseer, André Kaup. A Data Set Providing Synthetic and Real-World Fisheye Video Sequences [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/709

REGION MATCHING AND SIMILARITY ENHANCING FOR IMAGE RETRIEVAL


Many image retrieval systems adopt the bag-of-words model and rely on matching of local descriptors. However, these descriptors of keypoints, such as SIFT, may lead to false matches, since they do not consider the contextual information of the keypoints. In this paper, we incorporate the cues of meaningful regions where local descriptors are extracted. We describe a matching region estimation (MRE) method to find appropriate matching regions for local descriptor matching pairs.

Paper Details

Authors:
Guixuan Zhang, Zhi Zeng, Shuwu Zhang, Hu Guan, Qinzhen Guo
Submitted On:
16 March 2016 - 2:31am
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[1] Guixuan Zhang, Zhi Zeng, Shuwu Zhang, Hu Guan, Qinzhen Guo, "REGION MATCHING AND SIMILARITY ENHANCING FOR IMAGE RETRIEVAL", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/704. Accessed: Sep. 20, 2020.
@article{704-16,
url = {http://sigport.org/704},
author = {Guixuan Zhang; Zhi Zeng; Shuwu Zhang; Hu Guan; Qinzhen Guo },
publisher = {IEEE SigPort},
title = {REGION MATCHING AND SIMILARITY ENHANCING FOR IMAGE RETRIEVAL},
year = {2016} }
TY - EJOUR
T1 - REGION MATCHING AND SIMILARITY ENHANCING FOR IMAGE RETRIEVAL
AU - Guixuan Zhang; Zhi Zeng; Shuwu Zhang; Hu Guan; Qinzhen Guo
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/704
ER -
Guixuan Zhang, Zhi Zeng, Shuwu Zhang, Hu Guan, Qinzhen Guo. (2016). REGION MATCHING AND SIMILARITY ENHANCING FOR IMAGE RETRIEVAL. IEEE SigPort. http://sigport.org/704
Guixuan Zhang, Zhi Zeng, Shuwu Zhang, Hu Guan, Qinzhen Guo, 2016. REGION MATCHING AND SIMILARITY ENHANCING FOR IMAGE RETRIEVAL. Available at: http://sigport.org/704.
Guixuan Zhang, Zhi Zeng, Shuwu Zhang, Hu Guan, Qinzhen Guo. (2016). "REGION MATCHING AND SIMILARITY ENHANCING FOR IMAGE RETRIEVAL." Web.
1. Guixuan Zhang, Zhi Zeng, Shuwu Zhang, Hu Guan, Qinzhen Guo. REGION MATCHING AND SIMILARITY ENHANCING FOR IMAGE RETRIEVAL [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/704

Joint Instance and Feature Importance Re-weighting for Person Reidentification


Person re-identification refers to the task of recognizing the same person under different non-overlapping camera views and across different time and places.

Person reidentification refers to the task of recognizing the same person
under different non-overlapping camera views. Presently, person
reidentification based on metric learning is proved to be effective among
various techniques, which exploits the labeled data to learn
a subspace that maximizes the inter-person divergence while minimizes
the intra-person divergence. However, these methods fail to
take the different impacts of various instances and local features into
account. To address this issue, we propose to learn a projection matrix

Paper Details

Authors:
Qin Zhou, Shibao Zheng, Hua Yang, Yu Wang and Hang Su
Submitted On:
13 March 2016 - 8:54am
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[1] Qin Zhou, Shibao Zheng, Hua Yang, Yu Wang and Hang Su, "Joint Instance and Feature Importance Re-weighting for Person Reidentification", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/651. Accessed: Sep. 20, 2020.
@article{651-16,
url = {http://sigport.org/651},
author = {Qin Zhou; Shibao Zheng; Hua Yang; Yu Wang and Hang Su },
publisher = {IEEE SigPort},
title = {Joint Instance and Feature Importance Re-weighting for Person Reidentification},
year = {2016} }
TY - EJOUR
T1 - Joint Instance and Feature Importance Re-weighting for Person Reidentification
AU - Qin Zhou; Shibao Zheng; Hua Yang; Yu Wang and Hang Su
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/651
ER -
Qin Zhou, Shibao Zheng, Hua Yang, Yu Wang and Hang Su. (2016). Joint Instance and Feature Importance Re-weighting for Person Reidentification. IEEE SigPort. http://sigport.org/651
Qin Zhou, Shibao Zheng, Hua Yang, Yu Wang and Hang Su, 2016. Joint Instance and Feature Importance Re-weighting for Person Reidentification. Available at: http://sigport.org/651.
Qin Zhou, Shibao Zheng, Hua Yang, Yu Wang and Hang Su. (2016). "Joint Instance and Feature Importance Re-weighting for Person Reidentification." Web.
1. Qin Zhou, Shibao Zheng, Hua Yang, Yu Wang and Hang Su. Joint Instance and Feature Importance Re-weighting for Person Reidentification [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/651

IMAGE SENTIMENT ANALYSIS USING LATENT CORRELATIONS AMONG VISUAL, TEXTUAL, AND SENTIMENT VIEWS


Multi-View Embedding for Image Sentiment Analysis

As Internet users increasingly post images to express their daily sentiment and emotions, the analysis of sentiments in user-generated images is of increasing importance for developing several applications. Most conventional methods of image sentiment analysis focus on the design of visual features, and the use of text associated to the images has not been sufficiently investigated. This paper proposes a novel approach that exploits latent correlations among multiple views: visual and textual views, and a sentiment view constructed using SentiWordNet.

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Authors:
Marie Katsurai,Shin'ichi Satoh
Submitted On:
11 March 2016 - 9:18pm
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[1] Marie Katsurai,Shin'ichi Satoh, "IMAGE SENTIMENT ANALYSIS USING LATENT CORRELATIONS AMONG VISUAL, TEXTUAL, AND SENTIMENT VIEWS", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/619. Accessed: Sep. 20, 2020.
@article{619-16,
url = {http://sigport.org/619},
author = {Marie Katsurai;Shin'ichi Satoh },
publisher = {IEEE SigPort},
title = {IMAGE SENTIMENT ANALYSIS USING LATENT CORRELATIONS AMONG VISUAL, TEXTUAL, AND SENTIMENT VIEWS},
year = {2016} }
TY - EJOUR
T1 - IMAGE SENTIMENT ANALYSIS USING LATENT CORRELATIONS AMONG VISUAL, TEXTUAL, AND SENTIMENT VIEWS
AU - Marie Katsurai;Shin'ichi Satoh
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/619
ER -
Marie Katsurai,Shin'ichi Satoh. (2016). IMAGE SENTIMENT ANALYSIS USING LATENT CORRELATIONS AMONG VISUAL, TEXTUAL, AND SENTIMENT VIEWS. IEEE SigPort. http://sigport.org/619
Marie Katsurai,Shin'ichi Satoh, 2016. IMAGE SENTIMENT ANALYSIS USING LATENT CORRELATIONS AMONG VISUAL, TEXTUAL, AND SENTIMENT VIEWS. Available at: http://sigport.org/619.
Marie Katsurai,Shin'ichi Satoh. (2016). "IMAGE SENTIMENT ANALYSIS USING LATENT CORRELATIONS AMONG VISUAL, TEXTUAL, AND SENTIMENT VIEWS." Web.
1. Marie Katsurai,Shin'ichi Satoh. IMAGE SENTIMENT ANALYSIS USING LATENT CORRELATIONS AMONG VISUAL, TEXTUAL, AND SENTIMENT VIEWS [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/619

CRH: A Simple Benchmark Approach to Continuous Hashing

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23 February 2016 - 1:43pm
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[1] , "CRH: A Simple Benchmark Approach to Continuous Hashing", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/248. Accessed: Sep. 20, 2020.
@article{248-15,
url = {http://sigport.org/248},
author = { },
publisher = {IEEE SigPort},
title = {CRH: A Simple Benchmark Approach to Continuous Hashing},
year = {2015} }
TY - EJOUR
T1 - CRH: A Simple Benchmark Approach to Continuous Hashing
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/248
ER -
. (2015). CRH: A Simple Benchmark Approach to Continuous Hashing. IEEE SigPort. http://sigport.org/248
, 2015. CRH: A Simple Benchmark Approach to Continuous Hashing. Available at: http://sigport.org/248.
. (2015). "CRH: A Simple Benchmark Approach to Continuous Hashing." Web.
1. . CRH: A Simple Benchmark Approach to Continuous Hashing [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/248

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