Title

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

Publication Type

Journal Article

Publication Date

2007

Abstract

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.

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

Publication

International Journal of Image and Graphics

Volume

7

Issue

3

First Page

551

Last Page

581

ISSN

0219-4678

Identifier

10.1142/S0219467807002751

Publisher

World Scientific Publishing

Additional URL

http://dx.doi.org/10.1142/S0219467807002751

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