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QUANTITATIVE ANALYSIS OF CONTENT BASED IMAGE RETRIEVAL USING HOG, LBP AND GABOR FEATURE DESCRIPTORS IN COREL DATASET
Anto.A.Micheal, Pradeepthi.K.V

Published in: International Journal of Current Engineering And Scientific Research ( IJCESR)
Volume- 4, Issue-8, pp.57-64, Aug 2017
DPI :-> 16.10046.IJCESR.2017.V4I8.5764.1868



Abstract
The evolution of multimedia technology and rapidly increasing image collections on the Internet has attracted significant research efforts in image retrieval. Difficulties faced by text-based image retrieval motivated the researchers to develop new solutions for representation and indexing of visual information. This paper proposes a Content Based Image retrieval using HOG, LBP and Gabor features retrieve query related images. Method: This proposed approach extracts various features such as Gabor features, Histogram of Orientation Gradient (HOG) and Local Binary Pattern (LBP) from the images and they are formed as feature vectors. The relevance distance between the query image and images in the database are measured using similarity distance metrics known as Euclidean Distance, Mahalanobis Distance and Minkowski-Form Distance. Based on the similarity metrics, the images relevant to the query image are extracted. The proposed approach is experimented on COREL dataset and its performance is evaluated using statistical parameters such as, the precision and recall. Findings: The statistical results show that the precision and recall of the proposed system are 80.06% and 19.26% respectively. Novelty: The comparative study between the proposed content based image retrieval method and other existing methods indicates that proposed method proves its better feature extraction capability than the other image retrieval methods.

Key-Words / Index Term
Content Based Image Retrieval, Corel Dataset, HOG, LBP, Gabor, Similarity Metric

How to cite this article
Anto.A.Micheal, Pradeepthi.K.V , “QUANTITATIVE ANALYSIS OF CONTENT BASED IMAGE RETRIEVAL USING HOG, LBP AND GABOR FEATURE DESCRIPTORS IN COREL DATASET”, International Journal of Current Engineering And Scientific Research ( IJCESR), 4, Issue-8, pp.57-64, Aug 2017. DPI:16.10046.IJCESR.V4.I8.1868