Semantic-Based Facial Image-Retrieval System with Aid of Adaptive Particle Swarm Optimization and Squared Euclidian Distance

Manikandan Kalimuthu,Ilango Krishnamurthi

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.

Highlights

Due to the popularity of digital devices and the rise of social network/photo sharing services, the availability of consumer photos is increasing [1, 2]
Semantic features are used to retrieve images, there is a semantic gap in this technique. To fill this semantic gap in the existing technique, we propose an innovative method known as adaptive particle swarm optimization (APSO)
The proposed facial image-retrieval technique using APSO is implemented in the working platform of MATLAB with a machine configuration as follows: Processor: Intel Core i3

Summary

Introduction

Due to the popularity of digital devices and the rise of social network/photo sharing services, the availability of consumer photos is increasing [1, 2]. Law enforcement and criminal investigation agencies typically maintain large image databases of human faces [6]. Such databases consist of faces of individuals who have either committed crimes or are suspected of having been involved in criminal activities in the past. Composite drawings are used in identifying a potential suspect from an image database [7]. Retrieval from these databases is performed in the context of the following activities: the matching of composite drawings, Bann file searching, and the ranking of a photo lineup [1]

Results

Discussion

Conclusion