AUTHORS: N. T. Renukadevi
Download as PDF
ABSTRACT: Content Based Image Retrieval is a technique which uses visual contents for searching images from large scale image databases Information extracted from images such as color, texture and shape are known as feature vectors. Using multiple feature vectors to describe an image during retrieval process increases the accuracy when compared to the retrieval using single feature vector. The objective of this paper is to analyze the performance of coif let wavelet and Moment Invariant (MI) feature extraction methods and to evaluate the classification accuracy using Support Vector Machines (SVM) with Radial Basis Function kernel (RBF). Experiments were conducted on CT scan images of head, lung and stomach and the performance is investigated.
KEYWORDS: Coiflet wavelet, Content Based Image Retrieval (CBIR), Feature Extraction (FE), Moment Invariant (MI), Computed Tomography (CT), Support Vector Machines (SVM), Radial Basis Function kernel (RBF), Similiarity Measurement (SM)
REFERENCES:
[1]. Ardizzoni, S Bartolini, I & Patella, M, ‘WINDSURF : Region-based image retrieval using wavelets’, Proceedings of Tenth IEEE Workshop on Database and Expert Systems Applications, pp. 167-173, 1999.
[2]. Arun, K. S., and Menon, H. P. ‘Content based medical image retrieval by combining rotation invariant contourlet features and fourier descriptors’, International Journal of Recent Trends in Engineering, Volume 2, Issue 2, pp.35-39, 2001.
[3. Desai, P Pujari, J & Goudar, RH , ‘Image Retrieval using Wavelet based Shape Features’, Journal of Information Systems and Communication, vol.3, no.1, pp.162, 2012.
[4]. Ingrid Daubechies, Ten Lectures on Wavelets, Society for Industrial and Applied Mathematics, 1992.
[5]. Kekre, HB , ‘Image Retrieval using ColorTexture Features from DCT on VQ Code vectors obtained by Kekre’s Fast Codebook Generation’, ICGST-GVIP Journal, Volume 9, Issue 5, pp.1-8,2009.
[6]. Kundu, MK & Bagrecha, P, ‘Color Image Retrieval Using M-Band Wavelet Transform Based Color-Texture Feature’, Proceedings of Seventh IEEE International Conference on Advances in Pattern Recognition, pp.117-120, 2009.
[7]. Laura Keyes, ‘Using Moment Invariants for Classifying Shapes On large Scale Maps’ , Journal of Computers, Environment and Urban systems, vol.25, pp.119–130, 2006.
[8]. Małek, K et al , ‘The VIMOS Public Extragalactic Red shift Survey (VIPERS): A Support Vector Machine classification of galaxies, stars and AGNs’, Astronomy & Astro physics, vol. 557,no. A&A, pp.16, 2013.
[9]. Patil, RC & Sai, NST, ‘Image Retrieval using 2D Dual-Tree Discrete Wavelet Transform’ , International Journal of Computer Applications, vol. 14, no.6, pp.1-7, 2011.
[10]. Renukadevi, NT & Thangaraj, P , ‘Performance Analysis of Coiflet Wavelet and Moment Invariant Feature Extraction for CT Image Classification using SVM’, International Journal of IT, Engineering and Applied Sciences Research, vol.2, no.12, pp.1-6, 2013.
[11]. Stricker, MA & Orengo, M, ‘Similarity of Color Images, Storage and Retrieval for Image and Video Databases’, Proceedings of the Conference of Storage and Retrieval for Image and Video Databases, pp. 381-392, 1995.
[12]. Xia, S., Ge, D., Mo, W., and Zhang, Z., ‘A content-based retrieval system for endoscopic images’, Proceedings of the Conference of IEEE Engineering in Medicine and Biology Society, pp. 1720-1723, 2006.
[13]. Yue, J Li, Z Liu, & Fu, Z, ‘Content-based image retrieval using color and texture fused features’, Mathematical and Computer Modelling, vol.54, no.3, pp.1121-1127, 2011.
