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An Improved Image Retrieval System using Image Classification

by Ashish Oberio, Meenal Raheja
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 72 - Number 10
Year of Publication: 2013
Authors: Ashish Oberio, Meenal Raheja
10.5120/12533-9172

Ashish Oberio, Meenal Raheja . An Improved Image Retrieval System using Image Classification. International Journal of Computer Applications. 72, 10 ( June 2013), 48-55. DOI=10.5120/12533-9172

@article{ 10.5120/12533-9172,
author = { Ashish Oberio, Meenal Raheja },
title = { An Improved Image Retrieval System using Image Classification },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 72 },
number = { 10 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 48-55 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume72/number10/12533-9172/ },
doi = { 10.5120/12533-9172 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:37:36.437490+05:30
%A Ashish Oberio
%A Meenal Raheja
%T An Improved Image Retrieval System using Image Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 72
%N 10
%P 48-55
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

An image retrieval system is a computer system for browsing, searching and retrieving image using the actual content of image like visual features of an image as color, texture, shape, rotation, scaling factor and spatial layout. Now a days, retrieval of image from a large database are based on their visual similarity. The proposed Image retrieval system allows automatic extraction of target image according to object feature of the image itself. The proposed system is to improve the performance of image retrieval system using image classification. To improve existing image retrieval system, image decomposition, feature extraction and image matching mechanism should be improved. For image decomposition, modified Haar Wavelet Transform and D4 Wavelet Transform, to decompose color image into multilevel scale and for the conversion of wavelet coefficients has been used. Furthermore, progressive image retrieval strategy to achieve flexible CBIR is incorporated. The image feature are extracted by using Scale Invariant Feature Transform (SIFT). This approach relies on the choice of several parameters which directly impact its effectiveness when applied to retrieve image. Image matching is done by using NNS algorithm in KD-tree. The proposed system has demonstrated a improved image retrieval system on various database include WANG, MirFlickr, CLEF that containing approximately 15,000 color images.

References
  1. Y. Rui, T. S. Huang, M. Ortega, S. Mehrotra, 1998. "Relevance feedback: a power tool for interactive content-based image retrieval," IEEE Trans. Circuits Systems Video Technol. 8 (5), pp. 644–655
  2. J. Z. Wang, J. Li, G. Wiederhold, and O. Firschein, 1998,ªSystem for Screening Objectionable Images, " Computer Comm. ", vol. 21, no. 15, pp. 1355-1360
  3. A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain,"Content-based image retrieval at the end of the early years," IEEETrans. Dec. 2000 Pattern Anal. Mach. Intell. , vol. 22, no. 12, pp. 1349–1380,.
  4. James Z. Wang, Jia Li , SEPTEMBER2001 ,"SIMPLICITY: Semantics-Sensitive Integrated Matching for Picture Libraries", IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 23, NO. 9,
  5. K. Mikolajczyk, C. Schmid, "A performance evaluation of local descriptors," In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 257– 264, 2003.
  6. K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, L. V. Gool, 2005 , "A comparison of affine region detectors," Int. J. Comput. Vision vol. 65 , no. (1/2), pp. 43–76
  7. D. Nister and H. Stewenius. Scalable recognition with a kd-tree . 2006, In Proc. IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pp. 2161 – 2168.
  8. D. Nister and H. Stewenius, "Scalable Recognition with a Vocabulary Tree,2007," IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2161-2168, 2006. Y. Liu, D. Zhang, G. Lu, W. Y. Ma, "A survey of content-based image retrieval with high-level semantics, "Pattern Recognition, vol. 40, pp. 262-282.
  9. Z. Wang, K. Jia, P. Liu, "A Novel Image Retrieval Algorithm Based on ROI by using SIFT Feature Matching," International Conference on MultiMedia and Information Technology, IEEE Computer Society, Washington DC, pp. 338-341,2008.
  10. Deslaers, T. , Keysers, D. , Ney, H. : Features for image retrieval: an experimental comparison. Information Retrieval, vol. 11, 77–107. 2008
  11. Y. Fu and T. S Huang, "Image Classification using Correlation tensor analysis," IEEE trans. Image processing, Vol. 17, No. 2, pp. 226-234, Feb 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Image retrieval system SIFT Haar Wavelet transform D4 Wavelet Transform Feature Extraction Kd-tree Algorithm

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