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Image/Video Storage, Retrieval

Tracked Instance Search


In this work we propose tracking as a generic addition to the instance search task. From video data perspective, much information that can be used is not taken into account in the traditional instance search approach. This work aims to provide insights on exploiting such existing information by means of tracking and the proper combination of the results, independently of the instance search system. We also present a study on the improvement of the system when using multiple independent instances (up to 4) of the same person

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Authors:
Andreu Girbau, Ryota Hinami, Shin'ichi Satoh
Submitted On:
14 April 2018 - 4:04am
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[ICASSP 2018] Tracked Instance Search.pdf

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[1] Andreu Girbau, Ryota Hinami, Shin'ichi Satoh, "Tracked Instance Search ", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2811. Accessed: Sep. 20, 2020.
@article{2811-18,
url = {http://sigport.org/2811},
author = {Andreu Girbau; Ryota Hinami; Shin'ichi Satoh },
publisher = {IEEE SigPort},
title = {Tracked Instance Search },
year = {2018} }
TY - EJOUR
T1 - Tracked Instance Search
AU - Andreu Girbau; Ryota Hinami; Shin'ichi Satoh
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2811
ER -
Andreu Girbau, Ryota Hinami, Shin'ichi Satoh. (2018). Tracked Instance Search . IEEE SigPort. http://sigport.org/2811
Andreu Girbau, Ryota Hinami, Shin'ichi Satoh, 2018. Tracked Instance Search . Available at: http://sigport.org/2811.
Andreu Girbau, Ryota Hinami, Shin'ichi Satoh. (2018). "Tracked Instance Search ." Web.
1. Andreu Girbau, Ryota Hinami, Shin'ichi Satoh. Tracked Instance Search [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2811

APHASH ANCHOR-BASED PROBABILITY HASHING FOR IMAGE RETRIEVAL

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Authors:
Junjie Chen, Anran Wang, William K. Cheung
Submitted On:
13 April 2018 - 4:56am
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APHASH ANCHOR-BASED PROBABILITY HASHING FOR IMAGE RETRIEVAL.pdf

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[1] Junjie Chen, Anran Wang, William K. Cheung, "APHASH ANCHOR-BASED PROBABILITY HASHING FOR IMAGE RETRIEVAL", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2658. Accessed: Sep. 20, 2020.
@article{2658-18,
url = {http://sigport.org/2658},
author = {Junjie Chen; Anran Wang; William K. Cheung },
publisher = {IEEE SigPort},
title = {APHASH ANCHOR-BASED PROBABILITY HASHING FOR IMAGE RETRIEVAL},
year = {2018} }
TY - EJOUR
T1 - APHASH ANCHOR-BASED PROBABILITY HASHING FOR IMAGE RETRIEVAL
AU - Junjie Chen; Anran Wang; William K. Cheung
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2658
ER -
Junjie Chen, Anran Wang, William K. Cheung. (2018). APHASH ANCHOR-BASED PROBABILITY HASHING FOR IMAGE RETRIEVAL. IEEE SigPort. http://sigport.org/2658
Junjie Chen, Anran Wang, William K. Cheung, 2018. APHASH ANCHOR-BASED PROBABILITY HASHING FOR IMAGE RETRIEVAL. Available at: http://sigport.org/2658.
Junjie Chen, Anran Wang, William K. Cheung. (2018). "APHASH ANCHOR-BASED PROBABILITY HASHING FOR IMAGE RETRIEVAL." Web.
1. Junjie Chen, Anran Wang, William K. Cheung. APHASH ANCHOR-BASED PROBABILITY HASHING FOR IMAGE RETRIEVAL [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2658

QUERY EXPANSION WITH DIFFUSION ON MUTUAL RANK GRAPHS


In query expansion for object retrieval, there is substantial danger of query drift, where irrelevant information is inferred from pseudo-relevant images to enrich the query. To address this issue, we propose a query expansion method from the viewpoint of diffusion. It explores the structure of highly ranked images in a topological space, assuming that false positives reside on different manifolds from the query. For this purpose, a mutual rank graph is defined on pseudo-relevant images, and their distribution is learned by diffusing their query similarities through the graph.

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Authors:
Xiaomeng Wu, Go Irie, Kaoru Hiramatsu, and Kunio Kashino
Submitted On:
12 April 2018 - 10:03pm
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ICASSP18.2.pdf

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[1] Xiaomeng Wu, Go Irie, Kaoru Hiramatsu, and Kunio Kashino, "QUERY EXPANSION WITH DIFFUSION ON MUTUAL RANK GRAPHS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2554. Accessed: Sep. 20, 2020.
@article{2554-18,
url = {http://sigport.org/2554},
author = {Xiaomeng Wu; Go Irie; Kaoru Hiramatsu; and Kunio Kashino },
publisher = {IEEE SigPort},
title = {QUERY EXPANSION WITH DIFFUSION ON MUTUAL RANK GRAPHS},
year = {2018} }
TY - EJOUR
T1 - QUERY EXPANSION WITH DIFFUSION ON MUTUAL RANK GRAPHS
AU - Xiaomeng Wu; Go Irie; Kaoru Hiramatsu; and Kunio Kashino
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2554
ER -
Xiaomeng Wu, Go Irie, Kaoru Hiramatsu, and Kunio Kashino. (2018). QUERY EXPANSION WITH DIFFUSION ON MUTUAL RANK GRAPHS. IEEE SigPort. http://sigport.org/2554
Xiaomeng Wu, Go Irie, Kaoru Hiramatsu, and Kunio Kashino, 2018. QUERY EXPANSION WITH DIFFUSION ON MUTUAL RANK GRAPHS. Available at: http://sigport.org/2554.
Xiaomeng Wu, Go Irie, Kaoru Hiramatsu, and Kunio Kashino. (2018). "QUERY EXPANSION WITH DIFFUSION ON MUTUAL RANK GRAPHS." Web.
1. Xiaomeng Wu, Go Irie, Kaoru Hiramatsu, and Kunio Kashino. QUERY EXPANSION WITH DIFFUSION ON MUTUAL RANK GRAPHS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2554

A FEATURE EMBEDDING STRATEGY FOR HIGH-LEVEL CNN REPRESENTATIONS FROM MULTIPLE CONVNETS

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Authors:
Thangarajah Akilan, Jonathan Wu, Wei Jiang
Submitted On:
13 November 2017 - 9:13pm
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Transfer learning and feature embedding

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[1] Thangarajah Akilan, Jonathan Wu, Wei Jiang, " A FEATURE EMBEDDING STRATEGY FOR HIGH-LEVEL CNN REPRESENTATIONS FROM MULTIPLE CONVNETS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2345. Accessed: Sep. 20, 2020.
@article{2345-17,
url = {http://sigport.org/2345},
author = {Thangarajah Akilan; Jonathan Wu; Wei Jiang },
publisher = {IEEE SigPort},
title = { A FEATURE EMBEDDING STRATEGY FOR HIGH-LEVEL CNN REPRESENTATIONS FROM MULTIPLE CONVNETS},
year = {2017} }
TY - EJOUR
T1 - A FEATURE EMBEDDING STRATEGY FOR HIGH-LEVEL CNN REPRESENTATIONS FROM MULTIPLE CONVNETS
AU - Thangarajah Akilan; Jonathan Wu; Wei Jiang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2345
ER -
Thangarajah Akilan, Jonathan Wu, Wei Jiang. (2017). A FEATURE EMBEDDING STRATEGY FOR HIGH-LEVEL CNN REPRESENTATIONS FROM MULTIPLE CONVNETS. IEEE SigPort. http://sigport.org/2345
Thangarajah Akilan, Jonathan Wu, Wei Jiang, 2017. A FEATURE EMBEDDING STRATEGY FOR HIGH-LEVEL CNN REPRESENTATIONS FROM MULTIPLE CONVNETS. Available at: http://sigport.org/2345.
Thangarajah Akilan, Jonathan Wu, Wei Jiang. (2017). " A FEATURE EMBEDDING STRATEGY FOR HIGH-LEVEL CNN REPRESENTATIONS FROM MULTIPLE CONVNETS." Web.
1. Thangarajah Akilan, Jonathan Wu, Wei Jiang. A FEATURE EMBEDDING STRATEGY FOR HIGH-LEVEL CNN REPRESENTATIONS FROM MULTIPLE CONVNETS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2345

End-to-end Learning Binary Representation via Direct Binary Embedding


Learning binary representation is essential to large-scale computer vision tasks. Most existing algorithms require a separate quantization constraint to learn effective hashing functions. In this work, we present Direct Binary Embedding (DBE), a simple yet very effective algorithm to learn binary representation in an end-to-end fashion. By appending an ingeniously designed DBE layer to the deep convolutional neural network (DCNN), DBE learns binary code directly from the continuous DBE layer activation without quantization error.

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Authors:
Hairong Qi
Submitted On:
16 September 2017 - 12:01pm
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Liu_icip_17.pdf

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[1] Hairong Qi, "End-to-end Learning Binary Representation via Direct Binary Embedding", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2199. Accessed: Sep. 20, 2020.
@article{2199-17,
url = {http://sigport.org/2199},
author = {Hairong Qi },
publisher = {IEEE SigPort},
title = {End-to-end Learning Binary Representation via Direct Binary Embedding},
year = {2017} }
TY - EJOUR
T1 - End-to-end Learning Binary Representation via Direct Binary Embedding
AU - Hairong Qi
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2199
ER -
Hairong Qi. (2017). End-to-end Learning Binary Representation via Direct Binary Embedding. IEEE SigPort. http://sigport.org/2199
Hairong Qi, 2017. End-to-end Learning Binary Representation via Direct Binary Embedding. Available at: http://sigport.org/2199.
Hairong Qi. (2017). "End-to-end Learning Binary Representation via Direct Binary Embedding." Web.
1. Hairong Qi. End-to-end Learning Binary Representation via Direct Binary Embedding [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2199

Slides for ID 2913 at ICIP 2017

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Authors:
Junfu Pu, Yusuke Matsui, Fan Yang, Shin'ichi Satoh
Submitted On:
15 September 2017 - 8:28pm
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ICIP2017_ID2913.pdf

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[1] Junfu Pu, Yusuke Matsui, Fan Yang, Shin'ichi Satoh, "Slides for ID 2913 at ICIP 2017", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2163. Accessed: Sep. 20, 2020.
@article{2163-17,
url = {http://sigport.org/2163},
author = {Junfu Pu; Yusuke Matsui; Fan Yang; Shin'ichi Satoh },
publisher = {IEEE SigPort},
title = {Slides for ID 2913 at ICIP 2017},
year = {2017} }
TY - EJOUR
T1 - Slides for ID 2913 at ICIP 2017
AU - Junfu Pu; Yusuke Matsui; Fan Yang; Shin'ichi Satoh
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2163
ER -
Junfu Pu, Yusuke Matsui, Fan Yang, Shin'ichi Satoh. (2017). Slides for ID 2913 at ICIP 2017. IEEE SigPort. http://sigport.org/2163
Junfu Pu, Yusuke Matsui, Fan Yang, Shin'ichi Satoh, 2017. Slides for ID 2913 at ICIP 2017. Available at: http://sigport.org/2163.
Junfu Pu, Yusuke Matsui, Fan Yang, Shin'ichi Satoh. (2017). "Slides for ID 2913 at ICIP 2017." Web.
1. Junfu Pu, Yusuke Matsui, Fan Yang, Shin'ichi Satoh. Slides for ID 2913 at ICIP 2017 [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2163

LEARNING CIRCULANT SUPPORT VECTOR MACHINES FOR FAST IMAGE SEARCH


Binary hashing is an established approach for fast, approximate image search. It maps a query image to a binary vector so that Hamming distances approximate image similarities. Applying the hash function can be made fast by using a circulant matrix and the fast Fourier transform, but this circulant hash function must be learned optimally from training data. We show that a previously proposed learning algorithm based on optimization in the frequency domain is suboptimal.

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Authors:
Ramin Raziperchikolaei, Miguel Carreira-Perpinan
Submitted On:
15 September 2017 - 7:36pm
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icip17b-slides.pdf

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[1] Ramin Raziperchikolaei, Miguel Carreira-Perpinan, "LEARNING CIRCULANT SUPPORT VECTOR MACHINES FOR FAST IMAGE SEARCH", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2162. Accessed: Sep. 20, 2020.
@article{2162-17,
url = {http://sigport.org/2162},
author = {Ramin Raziperchikolaei; Miguel Carreira-Perpinan },
publisher = {IEEE SigPort},
title = {LEARNING CIRCULANT SUPPORT VECTOR MACHINES FOR FAST IMAGE SEARCH},
year = {2017} }
TY - EJOUR
T1 - LEARNING CIRCULANT SUPPORT VECTOR MACHINES FOR FAST IMAGE SEARCH
AU - Ramin Raziperchikolaei; Miguel Carreira-Perpinan
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2162
ER -
Ramin Raziperchikolaei, Miguel Carreira-Perpinan. (2017). LEARNING CIRCULANT SUPPORT VECTOR MACHINES FOR FAST IMAGE SEARCH. IEEE SigPort. http://sigport.org/2162
Ramin Raziperchikolaei, Miguel Carreira-Perpinan, 2017. LEARNING CIRCULANT SUPPORT VECTOR MACHINES FOR FAST IMAGE SEARCH. Available at: http://sigport.org/2162.
Ramin Raziperchikolaei, Miguel Carreira-Perpinan. (2017). "LEARNING CIRCULANT SUPPORT VECTOR MACHINES FOR FAST IMAGE SEARCH." Web.
1. Ramin Raziperchikolaei, Miguel Carreira-Perpinan. LEARNING CIRCULANT SUPPORT VECTOR MACHINES FOR FAST IMAGE SEARCH [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2162

ICIP2017_quasi rate distortion optimization for binary hashing

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Authors:
Yiding Liu, Wengang Zhou, and Houqiang Li
Submitted On:
15 September 2017 - 4:54pm
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ICIP17_Quasi Rate Distortion Optimization for Binary Hashing.pdf

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[1] Yiding Liu, Wengang Zhou, and Houqiang Li, "ICIP2017_quasi rate distortion optimization for binary hashing", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2160. Accessed: Sep. 20, 2020.
@article{2160-17,
url = {http://sigport.org/2160},
author = {Yiding Liu; Wengang Zhou; and Houqiang Li },
publisher = {IEEE SigPort},
title = {ICIP2017_quasi rate distortion optimization for binary hashing},
year = {2017} }
TY - EJOUR
T1 - ICIP2017_quasi rate distortion optimization for binary hashing
AU - Yiding Liu; Wengang Zhou; and Houqiang Li
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2160
ER -
Yiding Liu, Wengang Zhou, and Houqiang Li. (2017). ICIP2017_quasi rate distortion optimization for binary hashing. IEEE SigPort. http://sigport.org/2160
Yiding Liu, Wengang Zhou, and Houqiang Li, 2017. ICIP2017_quasi rate distortion optimization for binary hashing. Available at: http://sigport.org/2160.
Yiding Liu, Wengang Zhou, and Houqiang Li. (2017). "ICIP2017_quasi rate distortion optimization for binary hashing." Web.
1. Yiding Liu, Wengang Zhou, and Houqiang Li. ICIP2017_quasi rate distortion optimization for binary hashing [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2160

SUPERVISED HASHING WITH JOINTLY LEARNING EMBEDDING AND QUANTIZATION


Compared with unsupervised hashing, supervised hashing commonly illustrates better accuracy in many real applica- tions by leveraging semantic (label) information. However, it is tough to solve the supervised hashing problem directly because it is essentially a discrete optimization problem. Some other works try to solve the discrete optimization problem directly using binary quadratic programming, but they are typically too complicated and time-consuming while some supervised hashing methods have to solve a relaxed continuous optimization problem by dropping the discrete con- straints.

Paper Details

Authors:
Xiang Xiang;Feng Wang;Trac D. Tran
Submitted On:
15 September 2017 - 11:51am
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ICIP3325.pdf

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[1] Xiang Xiang;Feng Wang;Trac D. Tran, "SUPERVISED HASHING WITH JOINTLY LEARNING EMBEDDING AND QUANTIZATION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2147. Accessed: Sep. 20, 2020.
@article{2147-17,
url = {http://sigport.org/2147},
author = {Xiang Xiang;Feng Wang;Trac D. Tran },
publisher = {IEEE SigPort},
title = {SUPERVISED HASHING WITH JOINTLY LEARNING EMBEDDING AND QUANTIZATION},
year = {2017} }
TY - EJOUR
T1 - SUPERVISED HASHING WITH JOINTLY LEARNING EMBEDDING AND QUANTIZATION
AU - Xiang Xiang;Feng Wang;Trac D. Tran
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2147
ER -
Xiang Xiang;Feng Wang;Trac D. Tran. (2017). SUPERVISED HASHING WITH JOINTLY LEARNING EMBEDDING AND QUANTIZATION. IEEE SigPort. http://sigport.org/2147
Xiang Xiang;Feng Wang;Trac D. Tran, 2017. SUPERVISED HASHING WITH JOINTLY LEARNING EMBEDDING AND QUANTIZATION. Available at: http://sigport.org/2147.
Xiang Xiang;Feng Wang;Trac D. Tran. (2017). "SUPERVISED HASHING WITH JOINTLY LEARNING EMBEDDING AND QUANTIZATION." Web.
1. Xiang Xiang;Feng Wang;Trac D. Tran. SUPERVISED HASHING WITH JOINTLY LEARNING EMBEDDING AND QUANTIZATION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2147

An Object Based Graph Representation for Video Comparison


This paper develops a novel object based graph model for semantic video comparison. The model describes a video with detected objects as nodes, and elationship between the objects as edges in a graph. We investigated several spatial and temporal features as the graph node attributes, and dierent ways to describe the spatial-temporal relationship between objects as the edge attributes. To tackle the problem of erratic camera motion on the detected object, a global motion estimation and correction approach is proposed to reveal the true object trajectory.

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Authors:
Xin Feng, Yuanyi Xue, Yao Wang
Submitted On:
15 September 2017 - 11:50am
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VOG_poster_print_revised.pdf

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[1] Xin Feng, Yuanyi Xue, Yao Wang, "An Object Based Graph Representation for Video Comparison", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2145. Accessed: Sep. 20, 2020.
@article{2145-17,
url = {http://sigport.org/2145},
author = {Xin Feng; Yuanyi Xue; Yao Wang },
publisher = {IEEE SigPort},
title = {An Object Based Graph Representation for Video Comparison},
year = {2017} }
TY - EJOUR
T1 - An Object Based Graph Representation for Video Comparison
AU - Xin Feng; Yuanyi Xue; Yao Wang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2145
ER -
Xin Feng, Yuanyi Xue, Yao Wang. (2017). An Object Based Graph Representation for Video Comparison. IEEE SigPort. http://sigport.org/2145
Xin Feng, Yuanyi Xue, Yao Wang, 2017. An Object Based Graph Representation for Video Comparison. Available at: http://sigport.org/2145.
Xin Feng, Yuanyi Xue, Yao Wang. (2017). "An Object Based Graph Representation for Video Comparison." Web.
1. Xin Feng, Yuanyi Xue, Yao Wang. An Object Based Graph Representation for Video Comparison [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2145

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