Browse > Article
http://dx.doi.org/10.9717/kmms.2011.14.1.001

A Comparison of Global Feature Extraction Technologies and Their Performance for Image Identification  

Yang, Won-Keun (인하대학교 전자공학과)
Cho, A-Young (인하대학교 전자공학과)
Jeong, Dong-Seok (인하대학교 전자공학과)
Publication Information
Journal of Korea Multimedia Society / v.14, no.1, 2011 , pp. 1-14 More about this Journal
Abstract
While the circulation of images become active, various requirements to manage increasing database are raised. The content-based technology is one of methods to satisfy these requirements. The image is represented by feature vectors extracted by various methods in the content-based technology. The global feature method insures fast matching speed because the feature vector extracted by the global feature method is formed into a standard shape. The global feature extraction methods are classified into two categories, the spatial feature extraction and statistical feature extraction. And each group is divided by what kind of information is used, color feature or gray scale feature. In this paper, we introduce various global feature extraction technologies and compare their performance by accuracy, recall-precision graph, ANMRR, feature vector size and matching time. According to the experiments, the spatial features show good performance in non-geometrical modifications, and the extraction technologies that use color and histogram feature show the best performance.
Keywords
Global Feature; Performance Comparison; Image Identification;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 W.-K. Yang, A. Cho, D.-S. Jeong, and W.-G. Oh, "Image Description and Matching Scheme for Identical Image Searching," CONTENT 2009, pp.669-674, 2009.
2 ISO/IEC/JTC1/SC29/WG11:"Descriptionof Core Experiments for MPEG-7 Color/Texture Descriptors," MPEG document, N2929, 1999.
3 Manjunath, B.S., Ohm, J.-R., Vasudevan, V.V. and Yamada, A., "Color and texture descriptors," IEEE Transactions on Circuits and Systems for Video Technology, Vol.11, No.6, pp. 703-715, 2001.   DOI   ScienceOn
4 Chee Sun Won, Dong Kwon Park, and Soo-Jun Park, "Efficient Use of MPEG-7 Edge Histogram Descriptor," ETRI Journal, Vol.24, No. 1, pp.23-30, 2002.   DOI   ScienceOn
5 Cho-Huak The and Roland T. Chin, "On Image Analysis by the Methods of Moments," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.10, No.4, pp.496-513, 1988.   DOI   ScienceOn
6 Alireza Khotanzad and Yaw Hua Hong, "Invariant Image Recognition by Zernike Moments," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.12, No.5, pp.489-497, 1990.   DOI   ScienceOn
7 M.J. Swain and D.H. Ballard, "Color Indexing," International Journal of Computer Vision, Vol. 7, No.1, pp.11-32, 1991.   DOI   ScienceOn
8 Young Deok Chun, Sang Yong Seo, and Nam Chul Kim, "Image retrieval using BDIP and BVLC moments," IEEE Transactions on Circuits and Systems for Video Technology, Vol. 13, No.9, pp.951-957, 2003.   DOI   ScienceOn
9 Greg Pass, Ramin Zabih, and Justin Miller., "Comparing Images Using Color Coherence Vectors," ACM Multimedia 1996, pp.65-73, 1996.
10 Ojala. T., Aittola. M., and Matinmikko, E., "Empirical evaluation of MPEG-7 XM color descriptors in content-based retrieval of semantic image categories," ICPR 2002, Vol.16, No.2, pp.1021-1024, 2002.
11 C.C. Lin and S.S. Wang, "An Edge-based Copy Detection Scheme," Fundamenta Informaticae, Vol.83, No.3, pp.299-318, 2008.
12 S. J. Park, D. K. Park, and C. S. Won, "Core experiments on MPEG-7 edge histogram descriptor," MPEG document, M5984, 2000.
13 C. Kim, "Content-based Image copy detection," Signal Processing: Image Communication, Vol.18, No.3, pp.169-184, 2003.   DOI   ScienceOn
14 K. Wnukowicz, G. Galinski, and R. Tous, "Still Image Copy Detection Algorithm Robust to Basic Image Modifications," ELMAR-2008, pp. 455-458, 2008.
15 K. Wnukowicz, W. Skarbek, and G. Galinski, "Trajectory of Singular Energies for Image Replica Detection," SIGMAP 2007, pp. 444-449, 2007.
16 Ming-Ni Wu, Chia-Chen Lin, and Chin-Chen Chang, "A Robust Content-based Copy Detection Scheme," Fundamenta Informaticae, Vol. 71, No.2-3, pp.351-366, 2006.
17 A.Y. Cho, W.K. Yang, J.W. Lee, W.G. Oh, and D.S. Jeong, "Detection of copied Images using Concentric Circle Algorithm," CCSN 2008, pp. 13-16, 2008.
18 Samia G. Omar, Mohamed A. Ismail, and Sahar M. Ghanem, "WAY-LOOK4: A CBIR system based on class signature of the images' color and texture features," AICCSA-2009, pp.464-471, 2009.
19 Lei Wu, Jing Liu, Nenghai Yu, and Mingjing Li, "Query oriented subspace shifting for near-duplicate image detection," ICME 2008, pp. 661-664, 2008.
20 D.G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," International Journal of Computer Vision, Vol.60, No.2, pp. 91-110, 2004.   DOI
21 H. Bay, T. Tuytelaars, and L. Van Gool, "Surf: Speeded Up Robust Features," Proc. European Conference on Computer Vision 2006, LNCS 3951, pp.404-417, 2006
22 T. Deselaers, D. Keysers, and H. Ney, "Features for image retrieval: An experimental comparison," Information Retrieval, Vol.11, No. 2, pp.77-107, 2008.   DOI   ScienceOn