An Empirical Study of Query Effectiveness Improvement via Multiple Visual Feature Integration

This article is a comprehensive evaluation of a new framework for indexing image data, called CMVF, which can combine multiple data properties with a hybrid architecture. The goal of this system is to allow straightforward incorporation of multiple visual feature vectors, based on properties such as...

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Main Authors: SHEN, Jialie, Shepherd, John, Ngu, Anne H. 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/194
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spelling sg-smu-ink.sis_research-11932010-09-22T14:00:36Z An Empirical Study of Query Effectiveness Improvement via Multiple Visual Feature Integration SHEN, Jialie Shepherd, John Ngu, Anne H. H. This article is a comprehensive evaluation of a new framework for indexing image data, called CMVF, which can combine multiple data properties with a hybrid architecture. The goal of this system is to allow straightforward incorporation of multiple visual feature vectors, based on properties such as color, texture and shape, into a single low-dimension vector that is more effective for retrieval than the larger individual feature vectors. Moreover, CMVF is not only constrained to visual properties, but can also incorporate human classification criteria to further strengthen image retrieval process. The controlled study present in this paper concentrates on CMVF's performance on images, examining how the incorporation of extra features into the indexing affects both efficiency and effectiveness. Analysis and empirical evidence suggest that the inclusion of extra visual features can significantly improve system performance. Furthermore, it demonstrated that CMVF's effectiveness is robust against various kinds of common image distortions and initial (random) configuration of neural network. 2007-01-01T08:00:00Z text https://ink.library.smu.edu.sg/sis_research/194 info:doi/10.1142/S0219467807002751 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
Numerical Analysis and Scientific Computing
spellingShingle Databases and Information Systems
Numerical Analysis and Scientific Computing
SHEN, Jialie
Shepherd, John
Ngu, Anne H. H.
An Empirical Study of Query Effectiveness Improvement via Multiple Visual Feature Integration
description This article is a comprehensive evaluation of a new framework for indexing image data, called CMVF, which can combine multiple data properties with a hybrid architecture. The goal of this system is to allow straightforward incorporation of multiple visual feature vectors, based on properties such as color, texture and shape, into a single low-dimension vector that is more effective for retrieval than the larger individual feature vectors. Moreover, CMVF is not only constrained to visual properties, but can also incorporate human classification criteria to further strengthen image retrieval process. The controlled study present in this paper concentrates on CMVF's performance on images, examining how the incorporation of extra features into the indexing affects both efficiency and effectiveness. Analysis and empirical evidence suggest that the inclusion of extra visual features can significantly improve system performance. Furthermore, it demonstrated that CMVF's effectiveness is robust against various kinds of common image distortions and initial (random) configuration of neural network.
format text
author SHEN, Jialie
Shepherd, John
Ngu, Anne H. H.
author_facet SHEN, Jialie
Shepherd, John
Ngu, Anne H. H.
author_sort SHEN, Jialie
title An Empirical Study of Query Effectiveness Improvement via Multiple Visual Feature Integration
title_short An Empirical Study of Query Effectiveness Improvement via Multiple Visual Feature Integration
title_full An Empirical Study of Query Effectiveness Improvement via Multiple Visual Feature Integration
title_fullStr An Empirical Study of Query Effectiveness Improvement via Multiple Visual Feature Integration
title_full_unstemmed An Empirical Study of Query Effectiveness Improvement via Multiple Visual Feature Integration
title_sort empirical study of query effectiveness improvement via multiple visual feature integration
publisher Institutional Knowledge at Singapore Management University
publishDate 2007
url https://ink.library.smu.edu.sg/sis_research/194
_version_ 1770568916145274880