The Review Research on the Image Retrieval System Methodology

International Journal of Computer Trends and Technology (IJCTT)          
© 2016 by IJCTT Journal
Volume-41 Number-1
Year of Publication : 2016
Authors : Shashirekha.B, Dr.K.V.N.Sunitha


Shashirekha.B, Dr.K.V.N.Sunitha "The Review Research on the Image Retrieval System Methodology". International Journal of Computer Trends and Technology (IJCTT) V41(1):1-9, November 2016. ISSN:2231-2803. Published by Seventh Sense Research Group.

Abstract -
The gigantic development in the volume of images and additionally the broad application in different fields, the necessity for improvement of image retrieval strategies have upgraded. The capacity to handle a lot of image information is vital for image investigation and retrieval application. It turns out to be progressively vital to grow new CBIR (Based Image Retrieval) procedures that are powerful and adaptable for real time preparing of huge image accumulations. Based image retrieval system, proposed an answer for a substantial database of images which gives secure, productive and viable hunt and recover the comparative images of Query image from the database. In this paper we give an outline of the major hypotheses and rising strategies for Image Retrieval, distinctive sorts of image retrieval, and also a few augmented work in these territories. Content Based Image Retrieval (CBIR) is a standout amongst the most well-known and fascinating exploration ranges in light of the expansion of video and image information in computerized structure. Quick and precise retrieval of image from vast databases is an essential issue that should be tended to. The HOG technique is utilized to recover the component of image vectors and others. In this paper, the HOG strategy is completely examined and demonstrates its exactness and proficiency of image retrieval with lessened number of steps. Fundamentally, CBIR is on creating advancements to connect the semantic crevice that at present anticipates wide-sending of image -based internet searchers. Image web crawlers presently being used, for example, Google Images and Yahoo! Image inquiry are based on text annotation of images.

[1] IEEE Content based image retrieval: A past, present and new feature descriptor Manish K. Shriwas, V. R. Raut 19-20 March 2015.
[2] IEEE Content Based Image Retrieval on Hadoop Framework U.S.N. Raju, Shibin George, V. SairamPraneeth ,RanjeetDeo,Priyanka Jain 27 June-2 July 2015.
[3] IEEE Content-Based Image Retrieval Using Features Extracted From Halftoning-Based Block Truncation Coding Jing-Ming Guo, Senior Member, IEEE, and HeriPrasetyo DOI 10.1109/TIP.2014.2372619, IEEE Transactions on Image Processing.
[4] IEEE An integrated approach to Content Based Image Retrieval RoshiChoudhary ; Nikita Raina ; Neeshu Chaudhary ; Rashmi Chauhan ; R H Goudar24-27 Sept. 2014.
[5] T.P. Minka and R.W. Piccard. A society of models for video and image libraries. Technical Report 349, M.I.T. Media Laboratory Perceptual Computing Section, 1996.
[6] J.R. Smith and S.-F. Chang. VisualSEEk: a fully automated contentbased image query system. In Proc. The Fourth ACM International Multimedia Conference, pages 87–98, November 1996.
[7] H. Greenspan C. Carson, S. Belongie and J. Malik. Regionbased image querying. In IEEE Workshop on Content-based Access of Image and Video Libraries, Puerto Rico, June 1997.
[8] W. Y. Ma and B. S. Manjunath. NETRA: A toolbox for navigating large image databases. In IEEE International Conference on Image Processing, 1997.
[9] A. Finkelstein C.E. Jacobs and D.H. Salesin. Fast multiresolution image querying. In Computer graphics proceeding of SIGGRAPH, pages 278–280, Los Angeles, 1995.
[10] K.-C. Liang and C.-C. Jay Kuo. WaveGuide: A joint wavelet image description and representation system. 19 98. to appear.

Image Retrieval, Data Mining, Image Mining, KDD, knowledge discovery database, CBIR.