 Semantic‐Based Facial Image‐Retrieval System with Aid of Adaptive Particle Swarm Optimization and Squared Euclidian DistanceManikandan Kalimuthu and Ilango Krishnamurthi Journal of Applied Mathematics, 2015, vol. 2015, issue 1 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. Date: 2015 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:wly:jnljam:v:2015:y:2015:i:1:n:284378 Access Statistics for this article More articles in Journal of Applied Mathematics from John Wiley & Sons |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.