Content Based Image Retrieval using Collaborative Color, Texture and Shape Features
Kishor B. Bhangale1, Mohanaprasad K.2

1Kishor B. Bhangale*, Department of E&TC, D. Y. Patil College of Engineering, Akurdi, Pune, India.
2Dr. Mohanaprasad K., School of Electronics Engineering, VIT University, Chennai, India.
Manuscript received on December 17, 2019. | Revised Manuscript received on December 26, 2019. | Manuscript published on January 10, 2020. | PP: 1466-1469 | Volume-9 Issue-3, January 2020. | Retrieval Number: B8014129219/2020©BEIESP | DOI: 10.35940/ijitee.B8014.019320
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Abstract: 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. 
Keywords: Color Histogram, Image Retrieval, LBP, HOG, Support Vector Machine.
Scope of the Article:  Collaborative applications