Semantic-Based Facial Image-Retrieval System with Aid of Adaptive Particle Swarm Optimization and Squared Euclidian Distance
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 r...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2015/284378 |
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| author | Manikandan Kalimuthu Ilango Krishnamurthi |
| author_facet | Manikandan Kalimuthu Ilango Krishnamurthi |
| author_sort | Manikandan Kalimuthu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-33e0036811fb484ab07049bb0ad6d623 |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-33e0036811fb484ab07049bb0ad6d6232025-08-20T03:55:12ZengWileyJournal of Applied Mathematics1110-757X1687-00422015-01-01201510.1155/2015/284378284378Semantic-Based Facial Image-Retrieval System with Aid of Adaptive Particle Swarm Optimization and Squared Euclidian DistanceManikandan Kalimuthu0Ilango Krishnamurthi1Sri Krishna College of Engineering & Technology, Coimbatore 641008, IndiaSri Krishna College of Engineering & Technology, Coimbatore 641008, IndiaThe 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.http://dx.doi.org/10.1155/2015/284378 |
| spellingShingle | Manikandan Kalimuthu Ilango Krishnamurthi Semantic-Based Facial Image-Retrieval System with Aid of Adaptive Particle Swarm Optimization and Squared Euclidian Distance Journal of Applied Mathematics |
| title | Semantic-Based Facial Image-Retrieval System with Aid of Adaptive Particle Swarm Optimization and Squared Euclidian Distance |
| title_full | Semantic-Based Facial Image-Retrieval System with Aid of Adaptive Particle Swarm Optimization and Squared Euclidian Distance |
| title_fullStr | Semantic-Based Facial Image-Retrieval System with Aid of Adaptive Particle Swarm Optimization and Squared Euclidian Distance |
| title_full_unstemmed | Semantic-Based Facial Image-Retrieval System with Aid of Adaptive Particle Swarm Optimization and Squared Euclidian Distance |
| title_short | Semantic-Based Facial Image-Retrieval System with Aid of Adaptive Particle Swarm Optimization and Squared Euclidian Distance |
| title_sort | semantic based facial image retrieval system with aid of adaptive particle swarm optimization and squared euclidian distance |
| url | http://dx.doi.org/10.1155/2015/284378 |
| work_keys_str_mv | AT manikandankalimuthu semanticbasedfacialimageretrievalsystemwithaidofadaptiveparticleswarmoptimizationandsquaredeuclidiandistance AT ilangokrishnamurthi semanticbasedfacialimageretrievalsystemwithaidofadaptiveparticleswarmoptimizationandsquaredeuclidiandistance |