Facial Image Retrieval on Semantic Features Using Adaptive Genetic Algorithm

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, Fmeasure, 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 algorithm (PSO) and genetic algorithm (GA) and found to perform better; thus, proving its efficiency.

Optimization of discrete wavelet transform features using artificial bee colony algorithm for texture image classification

“…DWT is decomposed an image into four sub-bands. These sub-bands are marked as approximation coefficient (LL) which is the original image at lower resolution and three high frequency sub-bands corresponding to horizontal (HL) which highlights the horizontal edges of the original image, vertical (LH) which highlights the vertical edges and diagonal details (HH) which highlights the diagonal edges [24]. Once the image is divided into sub-band, any number of features can be extracted from the transformed image.…”