An Efficient and Generalized approach for Content Based Image Retrieval in MatLab

Автор: Shriram K V, P.L.K Priyadarsini, Subashri V

Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp

Статья в выпуске: 4 vol.4, 2012 года.

Бесплатный доступ

There is a serious flaw in existing image search engines, since they basically work under the influence of keywords. Retrieving images based on the keywords is not only inappropriate, but also time consuming. Content Based Image Retrieval (CBIR) is still a research area, which aims to retrieve images based on the content of the query image. In this paper we have proposed a CBIR based image retrieval system, which analyses innate properties of an image such as, the color, texture and the entropy factor, for efficient and meaningful image retrieval. The initial step is to retrieve images based on the color combination of the query image, which is followed by the texture based retrieval and finally, based on the entropy of the images, the results are filtered. The proposed system results in retrieving the images from the database which are similar to the query image. Entropy based image retrieval proved to be quite useful in filtering the irrelevant images thereby improving the efficiency of the system.

Еще

Image Processing, CBIR, Histogram, Wavelets, Quadratic distance, Euclidean distance, Entropy

Короткий адрес: https://sciup.org/15012292

IDR: 15012292

Список литературы An Efficient and Generalized approach for Content Based Image Retrieval in MatLab

  • Ja-Hwung Su, Wei-Jyun Huang, Philip S. Yu, and Vincent S. Tseng, ”Efficient Relevance Feedback for Content-Based Image Retrieval by Mining User Navigation Patterns” IEEE transactions on knowledge and data engineering, vol. 23, no. 3, march 2011.
  • Liana Stanescu, IADIS International Conference on Applied Computing, 2005 “on-line software system for content-based visual query of a color medical imagery.”
  • Neetesh Gupta, R.K.Singh, “A New Approach for CBIR Feedback based Image Classifier”, International Journal of Computer Applications (0975 – 8887) Volume 14– No.4, January 2011.
  • P.S.Suhasini, K.Sri Rama Krishna, and I. V. Murali Krishna, “CBIR using color histogram processing” Journal of Theoretical and Applied Information Technology, 2005-2009.
  • Rahul Metha, Nishkhol Mishra, Sanjeev Sharm, “Color – Texture based image retrieval system”, International Journal of Computer Applications (0975 – 8887), Volume 24 – No. 5 June 2011.
  • Satya Sai Prakash, RMD. Sundaram. Combining Novel features for Content Based Image Retrieval. IEEE XPLORE DIGITAL LIBRARY pages: 373 – 376, 12 November 2007.
  • Wasim Khan, Shiv Kumar. Neetesh Gupta, Nilofar Khan, “A Proposed Method for Image Retrieval using Histogram values and Texture Descriptor Analysis” , International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-I Issue-II, May 2011.
  • Gonzalez, R.C., Woods, R.E, “Digital Image Processing” 2nd Ed., Prentice Hall.
  • MatLab Manual.
  • http://pages.cs.wisc.edu/~beechung/docs/papers.html
  • http://www.ee.columbia.edu/~jrsmith/html/pubs/tatfcir/node22.html
  • A.M.W. Smeulders, M. Worring, S. Santini, A.Gupta, and R. Jain, “Content-based image retrieval at the end of early years, “IEEE Trans. On Pattern Analysis and machine intelligence, vol. 22, no. 12, december 2000.
  • Kondekar V. H., Kolkure V. S., Kore S.N. “Image Retrieval Techniques based on Image Features: A State of Art approach for CBIR”, International Journal of Computer Science and Information Security,Vol. 7, No. 1, 2010.
Еще
Статья научная