International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 5, No. 10, 8 Oct. 2013
Cover page and Table of Contents: PDF (size: 362KB)
A New Method for Content based Image Retrieval using Primitive Features
Full Text (PDF, 362KB), PP.36-42
Views: 0 Downloads: 0
Author(s)
Index Terms
Image retrieval, Color, Shape, Gradient Edge
Abstract
The diminishing expenditure of consumer electronic devices such as digital cameras and digital camcorders along with ease of transportation facilitated by the internet, has lead to a phenomenal rise in the quantity of multimedia data. The need to find a desired image from a collection is shared by many professional groups, including journalists, design engineers and art historians. While the requirements of image users, it can be characterize image queries into three levels. The proposed method based on primitive features such as color and shapes. These features are extracted and used as the basis for a similarity check between images. The shape and color features are extracted through Gradient Edge Detection and color histogram the combination of these features is robust. The experiment results show that the proposed image retrieval is more efficient and effective in retrieving the user interested images.
Cite This Paper
S.Maruthuperumal, G. Rosline Nesa Kumari, "A New Method for Content based Image Retrieval using Primitive Features", International Journal of Modern Education and Computer Science (IJMECS), vol.5, no.10, pp.36-42, 2013. DOI:10.5815/ijmecs.2013.10.05
Reference
[1]John Eakins and Margaret Graham, “Content-based Image Retrieval”, JISC Technology Applications Programme, University of Northumbria at Newcastle, January 1999 .
[2]Rui Y. & Huang T. S., Chang S. F, “Image retrieval: current techniques, directions, and open issues”, Journal of Visual Communication and Image Representation, 10, 39-62.
[3]Karin Kailing, Hans-Peter Kriegel and Stefan Schonauer, “ Content- based Image Retrieval Using Multiple Representations”. Proc. 8th Int. Conf. On Knowledge-Based Intelligent Information and Engineering Systems (KES’2004), Wellington, New Zealand, 2004, pp. 982-988. I
[4]Thomas Seidl and Hans-Peter Kriegel, “Efficient User-Adaptable Similarity Search in Large Multimedia Databases,” in Proceedings of the 23rd International Conference on Very Large Data Bases VLDB’97, Athens, Greece, August 1997, Found at: http://www.vldb.org/conf/1997/P506.PDF
[5]Yuri, T.S. Huarg and S.F. Chang, “Image retrieval: current techniques, promising directions and open issues”, Journal of Visual Communication and Image Representation, 10(4):39-62, 1999.
[6]F.Mahmoudi , J.Shanbehzadeh,A..M.Eftekhari Moghadam (2003), “Image retrieval based on shape similarity by edge orientation autocorrelogram”, Pattern Recognition 36 1725-1736.
[7]Y.Ruri, T.S. Huang and S.F.Chang, “Image Retrieval: current Techniques, promising directions, and open issues”, Journal of Communications and Image Representation, 10(1):39-62.March 1999.
[8]S.Loncaric, “A Survey of Shape Analysis Techniques”, Pattern Recognition, 31(8):983-1190 Aug 1998.
[9]D.Zhing and G.Lu, “ Review of Shape Representation and Description”, Pattern Recognition 37(1):1-19,Jan 2004.
[10]H.Nishida, “Structural feature indexing for retrieval of partially visible shapes”, pattern Recognitions 35(2002)55-67.
[11]R.Datta, D.Joshi, J.Li and J.Z.Wang, “Image retrieval: Ideas, influence, and trends of the new age”, ACM computing Survey, 40, no.2, pp.1-60, 2008.
[12]J.Eakins and M. Graham, “Content –Based Image Retrieval”, Techniques report, JISC Technology Application Program, 1999.
[13]A.M.Smeulders, M.Worring and S.Santini, A.Gupta and R.Jain, “Content base image retrieval at the early year”, IEEE Transaction on Pattern Analysis and Machine Intelligence, 22(12):pp.1349-1380,2000.
[14]Y.Liu, D.Zang, G.Lu and W.Y.Ma, “A survey of content base image retrieval with high-level semantics”, Pattern Recognition, Vol-40, pp-262-282, 2007.
[15]T.Kato, “Database architecture for content-base image retrieval”, In Proceeding of the SPIE-The International Society for Optical Engineering,vol.1662,pp.112-113,1992.
[16]Huang, P., and Jean.Y, “Using 2d c+-strings are spatial knowledge representation for image data base system”, 27, 1249-1257(1994).