The Effects of Multiple Query Evidences on Social Image Retrieval

System performance assessment and comparison are fundamental for large-scale image search engine development. This article documents a set of comprehensive empirical studies to explore the effects of multiple query evidences on large-scale social image search. The search performance based on the soc...

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Main Authors: CHENG, Zhiyong, SHEN, Jialie, MIAO, Haiyan
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2016
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Online Access:https://ink.library.smu.edu.sg/sis_research/2456
https://ink.library.smu.edu.sg/context/sis_research/article/3455/viewcontent/EffectsMultipleQueyEvidencesIR_2016_MS.pdf
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spelling sg-smu-ink.sis_research-34552020-01-14T13:22:16Z The Effects of Multiple Query Evidences on Social Image Retrieval CHENG, Zhiyong SHEN, Jialie MIAO, Haiyan System performance assessment and comparison are fundamental for large-scale image search engine development. This article documents a set of comprehensive empirical studies to explore the effects of multiple query evidences on large-scale social image search. The search performance based on the social tags, different kinds of visual features and their combinations are systematically studied and analyzed. To quantify the visual query complexity, a novel quantitative metric is proposed and applied to assess the influences of different visual queries based on their complexity levels. Besides, we also study the effects of automatic text query expansion with social tags using a pseudo relevance feedback method on the retrieval performance. Our analysis of experimental results shows a few key research findings: (1) social tag-based retrieval methods can achieve much better results than content-based retrieval methods; (2) a combination of textual and visual features can significantly and consistently improve the search performance; (3) the complexity of image queries has a strong correlation with retrieval results’ quality— more complex queries lead to poorer search effectiveness; and (4) query expansion based on social tags frequently causes search topic drift and consequently leads to performance degradation. 2016-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2456 info:doi/10.1007/s00530-014-0432-7 https://ink.library.smu.edu.sg/context/sis_research/article/3455/viewcontent/EffectsMultipleQueyEvidencesIR_2016_MS.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 Query evidence Social image retrieval Performance Evaluation Experimentation 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 Query evidence
Social image retrieval
Performance
Evaluation
Experimentation
Computer Sciences
Databases and Information Systems
spellingShingle Query evidence
Social image retrieval
Performance
Evaluation
Experimentation
Computer Sciences
Databases and Information Systems
CHENG, Zhiyong
SHEN, Jialie
MIAO, Haiyan
The Effects of Multiple Query Evidences on Social Image Retrieval
description System performance assessment and comparison are fundamental for large-scale image search engine development. This article documents a set of comprehensive empirical studies to explore the effects of multiple query evidences on large-scale social image search. The search performance based on the social tags, different kinds of visual features and their combinations are systematically studied and analyzed. To quantify the visual query complexity, a novel quantitative metric is proposed and applied to assess the influences of different visual queries based on their complexity levels. Besides, we also study the effects of automatic text query expansion with social tags using a pseudo relevance feedback method on the retrieval performance. Our analysis of experimental results shows a few key research findings: (1) social tag-based retrieval methods can achieve much better results than content-based retrieval methods; (2) a combination of textual and visual features can significantly and consistently improve the search performance; (3) the complexity of image queries has a strong correlation with retrieval results’ quality— more complex queries lead to poorer search effectiveness; and (4) query expansion based on social tags frequently causes search topic drift and consequently leads to performance degradation.
format text
author CHENG, Zhiyong
SHEN, Jialie
MIAO, Haiyan
author_facet CHENG, Zhiyong
SHEN, Jialie
MIAO, Haiyan
author_sort CHENG, Zhiyong
title The Effects of Multiple Query Evidences on Social Image Retrieval
title_short The Effects of Multiple Query Evidences on Social Image Retrieval
title_full The Effects of Multiple Query Evidences on Social Image Retrieval
title_fullStr The Effects of Multiple Query Evidences on Social Image Retrieval
title_full_unstemmed The Effects of Multiple Query Evidences on Social Image Retrieval
title_sort effects of multiple query evidences on social image retrieval
publisher Institutional Knowledge at Singapore Management University
publishDate 2016
url https://ink.library.smu.edu.sg/sis_research/2456
https://ink.library.smu.edu.sg/context/sis_research/article/3455/viewcontent/EffectsMultipleQueyEvidencesIR_2016_MS.pdf
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