Similarity Beyond Distance Measurement

One of the keys issues to content-based image retrieval is the similarity measurement of images. Images are represented as points in the space of low-level visual features and most similarity measures are based on certain distance measurement between these features. Given a distance metric, two imag...

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Main Authors: KANG, Feng, JIN, Rong, HOI, Steven C. H.
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2007
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Online Access:https://ink.library.smu.edu.sg/sis_research/2387
https://ink.library.smu.edu.sg/context/sis_research/article/3387/viewcontent/p449_kang__1_.pdf
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spelling sg-smu-ink.sis_research-33872016-01-13T04:39:54Z Similarity Beyond Distance Measurement KANG, Feng JIN, Rong HOI, Steven C. H. One of the keys issues to content-based image retrieval is the similarity measurement of images. Images are represented as points in the space of low-level visual features and most similarity measures are based on certain distance measurement between these features. Given a distance metric, two images with shorter distance are deemed to more similar than images that are far away. The well-known problem with these similarity measures is the semantic gap, namely two images separated by large distance could share the same semantic content. In this paper, we propose a novel similarity measure of images that goes beyond the distance measurement. The key idea is to exploit the clustering structure of images when a large number of images are present. The similarity of two images is determined not only by their Euclidean distance in the space of visual features but also by the likelihood for them to be clustered together, which is further estimated using a marginalized kernel. Our empirical studies with COREL datasets have shown that the proposed similarity measure is effective for traditional content-based image retrieval as well as user relevance feedback. 2007-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2387 https://ink.library.smu.edu.sg/context/sis_research/article/3387/viewcontent/p449_kang__1_.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Computer Sciences Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Computer Sciences
Databases and Information Systems
spellingShingle Computer Sciences
Databases and Information Systems
KANG, Feng
JIN, Rong
HOI, Steven C. H.
Similarity Beyond Distance Measurement
description One of the keys issues to content-based image retrieval is the similarity measurement of images. Images are represented as points in the space of low-level visual features and most similarity measures are based on certain distance measurement between these features. Given a distance metric, two images with shorter distance are deemed to more similar than images that are far away. The well-known problem with these similarity measures is the semantic gap, namely two images separated by large distance could share the same semantic content. In this paper, we propose a novel similarity measure of images that goes beyond the distance measurement. The key idea is to exploit the clustering structure of images when a large number of images are present. The similarity of two images is determined not only by their Euclidean distance in the space of visual features but also by the likelihood for them to be clustered together, which is further estimated using a marginalized kernel. Our empirical studies with COREL datasets have shown that the proposed similarity measure is effective for traditional content-based image retrieval as well as user relevance feedback.
format text
author KANG, Feng
JIN, Rong
HOI, Steven C. H.
author_facet KANG, Feng
JIN, Rong
HOI, Steven C. H.
author_sort KANG, Feng
title Similarity Beyond Distance Measurement
title_short Similarity Beyond Distance Measurement
title_full Similarity Beyond Distance Measurement
title_fullStr Similarity Beyond Distance Measurement
title_full_unstemmed Similarity Beyond Distance Measurement
title_sort similarity beyond distance measurement
publisher Institutional Knowledge at Singapore Management University
publishDate 2007
url https://ink.library.smu.edu.sg/sis_research/2387
https://ink.library.smu.edu.sg/context/sis_research/article/3387/viewcontent/p449_kang__1_.pdf
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