A Riview Paper on Content Based Image Retrieval Technique Using Color and Texture Feature
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2017 by IJETT Journal|
|Year of Publication : 2017|
|Authors : Nilima .R.Kharsan, Sagar.S.Badnerker
|DOI : 10.14445/22315381/IJETT-V43P246|
Nilima .R.Kharsan, Sagar.S.Badnerker "A Riview Paper on Content Based Image Retrieval Technique Using Color and Texture Feature", International Journal of Engineering Trends and Technology (IJETT), V43(5),274-278 January 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
There is a great need for developing an efficient technique for finding the images. In order to find an image, image has to be represented with certain features. Color, texture and shape are three important visual features of an image. I will implement an efficient image retrieval technique which uses dynamic dominant color, texture and shape features of an image. As a first step, an image is uniformly divided into 8 coarse partitions. The centroid of each partition is selected as its dominant color after the above coarse partition. By using Gray Level Co-occurrence Matrix (GLCM), texture of an image is obtained. Color and texture features are normalized. Using Gradient Vector Flow fields, shape information is captured in terms of edge images computed. To record the shape features, invariant moments are then used. A robust feature set for image retrieval is provided by using the combination of the color and texture features of an image in conjunction with the shape features.
 S. M. Metev and V. P. Veiko, Laser Assisted S. Vanitha Sivagami and K. Muneeswaran “Image Object Retrieval Using Distribution of Mixed Shape Descriptors” Middle-East Journal of Scientific Research 24 (4): 1057-1062, 2016.
 R. Durga Prasad, B.V.K. Sai Kumar, K. Sai Ram, B. Veera Manoj” Content Based Image Retrieval Using Dominant Color and Texture Features” International Journal for Modern Trends in Science and Technology, Volume: 2, Issue: 04,April 2016.
 Zhijie Zhao, Qin Tian, Huadong Sun, Xuesong Jin and Junxi Guo ,” Content Based Image Retrieval Scheme using Color, Texture and Shape Features”,International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol.9, No.1 (2016), pp.203-212, ijsip.2016.9.1.19.
 Sonali Mathur, Deepa Chaurse” Rank Based Image Retrieval Technique using Hue Saturation and Value (HSV) and Gray Level Co-occurrence Matrix (GLCM) Features”, www.rsisinternational.org ,Volume III, Issue IA, January 2016.
 Prof. Anil T. Lohar ,Prof. Rahul Diwate “Augmented Performance Analysis of Block Truncation Coding based Image Retrieval techniques through sundry similarity measures”,International Journal of Reaserch in Engineering ,Science and Technologies,Vol.1,No.6,March 2016.
 KattaSugamya,SureshPabboju, Dr.A.VinayaBabu.” A CBIR Classification using support vector machines”.International Conference on Advances in Human Machine Interaction(HMI - 2016),March 03-05, 2016,IEEE.
 Tatiana Jaworska,” How to Compare Search Engines in CBIR?”. SAI Computing Conference 2016July 13-15, 2016 | London, UK.
 Ekta Gupta, Rajendra Singh Kushwah “Combination of Global and Local Features using DWT with SVM for CBIR”. PAPER: 978-1-4673-7231-2/15/$31.00 ©2015 IEEE.
 Neelima Bagri and Punit Kumar Johari,” A Comparative Study on Feature Extraction using Texture and Shape for Content Based Image Retrieval”, Vol.80 (2015), pp.41-52,ijast,2015.
 S.Sasikala,R. Soniya Gandhi ,“ Efficient Content Based Image Retrieval System with Metadata Processing”, International Journal for Innovative Research in Science & Technology, Volume 1,| Issue 10, March 2015.
 Miss.Aboli W. Hole1, Prof Prabhakar L. Ramteke,“Content Based Image Retrieval using Dominant Color and Texture features”, International Journal of Advanced Research in Computer and Communication EngineeringVol. 4, Issue 10, October 2015.