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Facial Image Retrieval on Semantic Features Using Adaptive Genetic Algorithm

Authors :
Marwan Ali SHNAN
Taha H. RASSEM
Source :
Informatică economică, Vol 22, Iss 4, Pp 15-30 (2018)
Publication Year :
2018
Publisher :
Inforec Association, 2018.

Abstract

The emergence of larger databases has made image retrieval techniques an essential component, and has led to the development of more efficient image retrieval systems. Retrieval can be either content or text-based. In this paper, the focus is on the content-based image retrieval from the FGNET database. Input query images are subjected to several processing techniques in the database before computing the squared Euclidean distance (SED) between them. The images with the shortest Euclidean distance are considered as a match and are retrieved. The processing techniques involve the application of the median modified Weiner filter (MMWF), extraction of the low-level features using histogram-oriented gradients (HOG), discrete wavelet transform (DWT), GIST, and Local tetra pattern (LTrP). Finally, the features are selected using Viola-Jones algorithm. In this study, the average PSNR value obtained after applying Wiener filter was 45.29. The performance of the AGA was evaluated based on its precision, F-measure, and recall, and the obtained average values were respectively 0.75, 0.692, and 0.66. The performance matrix of the AGA was compared to those of particle swarm optimization al-gorithm (PSO) and genetic algorithm (GA) and found to perform better; thus, proving its effi-ciency.

Details

Language :
English, Romanian; Moldavian; Moldovan
ISSN :
14531305 and 18428088
Volume :
22
Issue :
4
Database :
Directory of Open Access Journals
Journal :
Informatică economică
Publication Type :
Academic Journal
Accession number :
edsdoj.74680538f8c24937b28cc621858d7b5a
Document Type :
article
Full Text :
https://doi.org/10.12948/issn14531305/22.4.2018.02