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2015 Semantic-Based Facial Image-Retrieval System with Aid of Adaptive Particle Swarm Optimization and Squared Euclidian Distance
Manikandan Kalimuthu, Ilango Krishnamurthi
J. Appl. Math. 2015: 1-12 (2015). DOI: 10.1155/2015/284378

Abstract

The semantic-based facial image-retrieval system is concerned with the process of retrieving facial images based on the semantic information of query images and database images. The image-retrieval systems discussed in the literature have some drawbacks that degrade the performance of facial image retrieval. To reduce the drawbacks in the existing techniques, we propose an efficient semantic-based facial image-retrieval (SFIR) system using APSO and squared Euclidian distance (SED). The proposed technique consists of three stages: feature extraction, optimization, and image retrieval. Initially, the features are extracted from the database images. Low-level features (shape, color, and texture) and high-level features (face, mouth, nose, left eye, and right eye) are the two features used in the feature-extraction process. In the second stage, a semantic gap between these features is reduced by a well-known adaptive particle swarm optimization (APSO) technique. Afterward, a squared Euclidian distance (SED) measure will be utilized to retrieve the face images that have less distance with the query image. The proposed semantic-based facial image-retrieval (SFIR) system with APSO-SED will be implemented in working platform of MATLAB, and the performance will be analyzed.

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Manikandan Kalimuthu. Ilango Krishnamurthi. "Semantic-Based Facial Image-Retrieval System with Aid of Adaptive Particle Swarm Optimization and Squared Euclidian Distance." J. Appl. Math. 2015 1 - 12, 2015. https://doi.org/10.1155/2015/284378

Information

Published: 2015
First available in Project Euclid: 13 October 2015

zbMATH: 07132053
MathSciNet: MR3399549
Digital Object Identifier: 10.1155/2015/284378

Rights: Copyright © 2015 Hindawi

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