Hyperlink-aware object retrieval

In this paper, we address the problem of object retrieval by hyperlinking the reference data set at subimage level. One of the main challenges in object retrieval involves small objects on cluttered backgrounds, where the similarity between the querying object and a relevant image can be heavily aff...

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Main Authors: ZHANG, Wei, NGO, Chong-wah, CAO, Xiaochun
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Language:English
Published: Institutional Knowledge at Singapore Management University 2016
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Online Access:https://ink.library.smu.edu.sg/sis_research/6421
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spelling sg-smu-ink.sis_research-74242021-11-23T01:36:02Z Hyperlink-aware object retrieval ZHANG, Wei NGO, Chong-wah CAO, Xiaochun In this paper, we address the problem of object retrieval by hyperlinking the reference data set at subimage level. One of the main challenges in object retrieval involves small objects on cluttered backgrounds, where the similarity between the querying object and a relevant image can be heavily affected by the background. To address this problem, we propose an efficient object retrieval technique by hyperlinking the visual entities among the reference data set. In particular, a two-step framework is proposed: subimage-level hyperlinking and hyperlink-aware reranking. For hyperlinking, we propose a scalable object mining technique using Thread-of-Features, which is designed for mining subimage-level objects. For reranking, the initial search results are reranked with a hyperlink-aware transition matrix encoding subimage-level connectivity. Through this framework, small objects can be retrieved effectively. Moreover, our method introduces only a tiny computation overhead to online processing, due to the sparse transition matrix. The proposed technique is featured by the novel perspective (object hyperlinking) for visual search, as well as the object hyperlinking technique. We demonstrate the effectiveness and efficiency of our hyperlinking and retrieval methods by experimenting upon several object-retrieval data sets. 2016-09-01T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/6421 info:doi/10.1109/TIP.2016.2590321 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Object retrieval hyperlinking re-ranking object mining Computer Sciences Graphics and Human Computer Interfaces
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Object retrieval
hyperlinking
re-ranking
object mining
Computer Sciences
Graphics and Human Computer Interfaces
spellingShingle Object retrieval
hyperlinking
re-ranking
object mining
Computer Sciences
Graphics and Human Computer Interfaces
ZHANG, Wei
NGO, Chong-wah
CAO, Xiaochun
Hyperlink-aware object retrieval
description In this paper, we address the problem of object retrieval by hyperlinking the reference data set at subimage level. One of the main challenges in object retrieval involves small objects on cluttered backgrounds, where the similarity between the querying object and a relevant image can be heavily affected by the background. To address this problem, we propose an efficient object retrieval technique by hyperlinking the visual entities among the reference data set. In particular, a two-step framework is proposed: subimage-level hyperlinking and hyperlink-aware reranking. For hyperlinking, we propose a scalable object mining technique using Thread-of-Features, which is designed for mining subimage-level objects. For reranking, the initial search results are reranked with a hyperlink-aware transition matrix encoding subimage-level connectivity. Through this framework, small objects can be retrieved effectively. Moreover, our method introduces only a tiny computation overhead to online processing, due to the sparse transition matrix. The proposed technique is featured by the novel perspective (object hyperlinking) for visual search, as well as the object hyperlinking technique. We demonstrate the effectiveness and efficiency of our hyperlinking and retrieval methods by experimenting upon several object-retrieval data sets.
format text
author ZHANG, Wei
NGO, Chong-wah
CAO, Xiaochun
author_facet ZHANG, Wei
NGO, Chong-wah
CAO, Xiaochun
author_sort ZHANG, Wei
title Hyperlink-aware object retrieval
title_short Hyperlink-aware object retrieval
title_full Hyperlink-aware object retrieval
title_fullStr Hyperlink-aware object retrieval
title_full_unstemmed Hyperlink-aware object retrieval
title_sort hyperlink-aware object retrieval
publisher Institutional Knowledge at Singapore Management University
publishDate 2016
url https://ink.library.smu.edu.sg/sis_research/6421
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