Highly Efficient Multimedia Image Retrieval using Slim Descriptor

K.T. Ahmed; H. Afzal; S. Iqbal; M.G. Hussain; M.R. Mufti; A. Karim

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Highly Efficient Multimedia Image Retrieval using Slim Descriptor

Author(s)

K.T. Ahmed Department of Computer Science, Bahauddin Zakariya University,Multan,Pakistan

H. Afzal Department of Computer Science, Bahauddin Zakariya University,Multan,Pakistan

S. Iqbal Department of Computer Science, Bahauddin Zakariya University,Multan,Pakistan

M.G. Hussain Department of Computer Science, Bahauddin Zakariya University,Multan,Pakistan

M.R. Mufti Department of Computer Science, COMSATS University Islamabad,Vehari Campus Vehari,Pakistan

A. Karim Department of Information Technology Bahauddin Zakariya University Multan, Pakistan

Abstract

Efficient multimedia image extraction with high precision compatible with diverse image datasets is an implicit requirement of current image retrieval systems. In this paper, a multimedia image descriptor is introduced to achieve high performance along with high accuracy. For this, Histograms of Oriented Gradients (HOG) are extracted from a dense grid partitioned image by taking edge intensity based orientation histograms as primitive feature vectors. We depleted these massive redundant candidates to linearly uncorrelated variables by applying orthogonal transformation to achieve Principal Components (PC) where succeeding component's constraint dependent orthogonal variance based local descriptors are compact and robust to deformation. A distinctness of our proposed approach is the selection of a single coefficient having largest variance as image descriptor out of returned dimensionally reduced vectors which results in higher performance and less space and time consumption. Supervised learning using Support Vector Machine (SVM) is then applied on nonprobabilistic binary linear classification of images. The experimental results show higher precision, low memory consumption and sufficient performance gain.

Publication Details

Page(s) 118-128

DOI DOI not available

Published Journal: Nucleus, Volume: 57, Issue: 4, Year: 2020

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Keywords

principal component analysis, Support Vector Machine, Dimension reduction, Computer vision, Histogram of oriented gradients, Multimedia images retrieval