Open AccessContent Based Image Retrieval using Collaborative Color, Texture and Shape Features
Author(s) -
Kishor B. Bhangale*,
Mohanaprasad K.
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.b8014.019320
Subject(s) - artificial intelligence , pattern recognition (psychology) , computer science , feature extraction , local binary patterns , preprocessor , histogram , content based image retrieval , image retrieval , color histogram , classifier (uml) , support vector machine , computer vision , image processing , color image , image (mathematics)
Selection of feature extraction method is incredibly recondite task in Content Based Image Retrieval (CBIR). In this paper, CBIR is implemented using collaboration of color; texture and shape attribute to improve the feature discriminating property. The implementation is divided in to three steps such as preprocessing, features extraction, classification. We have proposed color histogram features for color feature extraction, Local Binary Pattern (LBP) for texture feature extraction, and Histogram of oriented gradients (HOG) for shape attribute extraction. For the classification support vector machine classifier is applied. Experimental results show that combination of all three features outperforms the individual feature or combination of two feature extraction techniques