A compression based distance measure for texture

Proceedings of the 10th SIAM International Conference on Data Mining, SDM 2010

Volumn , Issue , 2010, Pages 850-861

  Campana, Bilson J L ^a   Keogh, Eamonn J ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Classification; Clustering; Image similarity; Video compression

Indexed keywords

CLASSIFICATION (OF INFORMATION); CLUSTERING ALGORITHMS; COMPUTATIONAL COMPLEXITY; DATA MINING; DNA SEQUENCES; FORENSIC SCIENCE; PETROLEUM RESERVOIR EVALUATION; TEXTURES;

CLUSTERING; EMPIRICAL EVALUATIONS; IMAGE SIMILARITY; KOLMOGOROV COMPLEXITY; SIMILARITY MEASURE; TEXTURE SIMILARITY; TEXTURE SIMILARITY MEASURE; VIDEO COMPRESSORS;

IMAGE COMPRESSION;

EID: 84874676749     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972801.74     Document Type: Conference Paper

References (50)

1. American Cancer Society, Breast Cancer Facts & Figures 2007-2008, American Cancer Society, Inc.
2. J. Bethony, S. Brooker, M. Albonico, S. Geiger, A. Loukas, D. Diement, P. J. Hotez, Soil-transmitted helminth infections: ascariasis, trichuriasis, and hookworm. The Lancet, 2006.
3. F. Bianconi, A. Fernandez, Evaluation of the effects of Gabor filter parameters on texture classification. Pattern Recognition 40(12), 3325-3335 (2007).
4. A. Bratko, G. Cormack, B. Filipic, T. Lynam, B. Zupan, Spam Filtering Using Statistical Data Compression Models, Journal of Machine Learning Research 7, Dec. 2006.
5. P. Brodatz, Textures: A Photographic Album for Artists and Designers, New York: Dover, 1966.
6. B. Campana, Website for this paper http://www.cs.ucr.edu/~bcampana/ texture.html.
7. Center for Artificial Intelligence and Robotics, http://www.cairo-aisb. com/.
8. R. Cilibrasi, P. Vitányi, Clustering by Compression, IEEE Transactions on Information Theory 51, 1523-1545, 2005.
9. K. J. Dana, B. van Ginneken, S. K. Nayar, J. J. Koenderink, Reflectance and texture of real-world surfaces, Reflectance and texture of real-world surfaces, 1, 1999.
10. D. Cerra, M. Datcu, A Model Conditioned Data Compression Based Similarity Measure, DCC, 2008.
11. M. Delalandre, J. Ogier, J. Lladós, A fast cbir system of old ornamental letter, In Workshop on Graphics Recognition (GREC), Vol. 5046 of Lecture Note in Computer Science (LNCS), pages 135-144, 2008.
12. Coding of moving pictures and associated audio. Committee Draft of Standard ISO 11172: ISO/MPEG 90/176, Dec. 1990.
13. P.R. Cohen, D. Jensen, Overfitting explained, In Prelim. Papers Sixth Intl. Workshop on Artificial Intelligence and Statistics, pages 115-122, January 1997.
14. P. De Ley, Assistant Professor and Assistant Nematologist at UCR, Personal communication, Feb 2009.
15. S.W. Duffy, L. Tabar, H. H. Chen, et al, The impact of organized mammography service screening on breast carcinoma mortality in seven Swedish counties, Cancer, 95(3):458-469, Aug 1 2002.
16. E. J. Keogh, S. Lonardi, C. A. Ratanamahatana, L. Wei, S. Lee, J. Handley, Compression-based data mining of sequential data, Data Min Knowl. Discov. 14(1): 99-129, 2007.
17. M. Garcia, A. Jemal, E. M. Ward, M. M. Center, Y. Hao, R. L. Siegel, M.J. Thun, Global Cancer Facts & Figures 2007, Atlanta, GA: American Cancer Society, 2007.
18. M. Giger, Current Issues in CAD Mammography. Digital Mammography, Proc. 3rd Int. Workshop of Digital Mammography, 1996.
19. E. Hayman, B. Caputo, M. Fritz, J. O. Eklundh, On the significance of real-world conditions for material classification, In: Proc. European Conf. on Computer Vision, No. 3, Springer-Verlag, pp. 253-266, 2004.
20. L. L. Humphrey, M. Helfand, B.K. Chan, S.H. Woolf, Breast cancer screening: a summary of the evidence for the U.S. Preventive Services Task Force, Ann Intern Med., 137 (5 Part 1):347-360, Sep 3 2002.
21. A. Jemal, R. Siegel, E. Ward, et al, Cancer Statistics, Cancer Statistics, 56:106-130, 2006.
22. I. Kavdir, Discrimination of sunflower, weed and soil by artificial neural networks, Computers and Electronics in Agriculture 44, 153-160, 2004.
23. M. Khalid, Design of an Intelligent Wood Species Recognition System, Technical Report of Center for Artificial Intelligence and Robotics (CAIRO), Malaysia, 2008.
24. N. Krasnogor, D. A. Pelta, Measuring the similarity of protein structures by means of the universal similarity metric, Bioinformatics, 20,: 1015-1021, 2004.
25. S. Lazebnik, C. Schmid, J. Ponce, A Sparse Texture Representation Using Local Affine Regions, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, no. 8, pp. 1265-1278, August 2005.
26. D. Le Gall, Mpeg: a video compression standard for multimedia application, Commun. ACM, Vol. 34, no. 4, pp. 46-58, April 1991.
27. T. Leung, J. Malik, Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons, Int. J. Comput. Vision 43, 1, 29-44. Jun. 2001.
28. M. Li, J. H. Badger, X. Chen, S. Kwong, P. Kearney, H. Zhang, An information-based sequence distance and its application to whole mitochondrial genome phylogeny, Bioinformatics 17: 149-154, 2001.
29. M. Li, X. Chen, X. Li, B. Ma, P. Vitanyi, The similarity metric, Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms, Pages: 863-872, 2003.
30. M. Li, P. Vitanyi, An Introduction to Kolmogorov Complexity and Its Applications. Second Edition, Springer Verlag, 1997.
31. P. Li, J. R. Flenley, Pollen texture identification using neural networks, Pollen texture identification using neural networks, 38:59-64, 1999.
32. A. Macedonas, D. Besiris, G. Economou, S. Fotopoulos, Dictionary based color image retrieval, Journal of Visual Communication and Image Representation, v. 19 n.7, p.464-470, October, 2008.
33. M. Mirmehdi, X. Xie, J. Suri, (eds.), Handbook of Texture Analysis, Imperial College Press., December 2008.
34. B. S. Manjunath, W. Y. Ma, Texture Features for Browsing and Retrieval of Image Data, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 18, no. 8, pp. 837-842, 1996.
35. M. Mayo, A. Watson, Automatic species identification of live moths. In Ellis et. al, editor, Proc. of the 26th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, 195-202, 2006.
36. MIT Vision and Modeling Group, http://vismod.media.mit.edu/vismod/.
37. M. Li, Y. Zhu, Image Classification Via LZ78 Based String Kernel: A Comparative Study, PAKDD, 704-712, 2006.
38. T. Pavlidis, Limitations of CBIR (Keynote Talk). International Conference on Pattern Recognition, 8-11, Tampa, Florida, Dec. 2006.
39. R. Porter, N. Canagarajah, Robust Rotation-Invariant Texture Classification: Wavelet, Gabor Filter and GMRF Based Schemes, Robust Rotation-Invariant Texture Classification: Wavelet, Gabor Filter and GMRF Based Schemes, 144:180-188, 1997.
40. T. Randen, Brodatz Textures Image Database, http://www.ux.uis.no/ ~tranden/brodatz.html.
41. Y. Rubner, C. Tomasi, L. Guibas, The Earth Mover's Distance as a Metric for Image Retrieval, International Journal of Computer Vision, v.40 n.2, p.99-121, Nov. 2000.
42. K. Russell, H. Do, J. Huff, N. Platnick, Introducing SPIDA-web: wavelets, neural networks and Internet accessibility in an image-based automated identification system, In N. MacLeod (ed), Automated Object Identification in Systematics: Theory, Approaches, and Applications. Springer Verlag, 2007.
43. O. Silven, M. Niskanen, H. Kauppinen, Wood inspection with non-supervised clustering, COST action E10 Workshop - Wood properties for industrial use, 18-22, Espoo, Finland, June 2000.
44. J. Suckling, The Mammographic Image Analysis Society Digital Mammogram Database, Exerpta Medica, International Congress Series 1069, pp375-378, 1994.
45. L. Tabar, M. F. Yen, B. Vitak, H. H. Chen, R. A. Smith, S. W. Duffy, Mammography service screening and mortality in breast cancer patients: 20-year follow-up before and after introduction of screening, Lancet, 1405-1410, Apr 26 2003.
46. J. Tou, P. Lau, Y. Tay. Computer Vision-based Wood Recognition System, Proceedings of International Workshop on Advanced Image Technology (IWAIT 2007), pp. 197-202, 2007.
47. M. Varma, A. Zisserman, A Statistical Approach to Texture Classification from Single Images, Int. J. Comput. Vision 62, 1-2, 61-81, Apr. 2005.
48. T. Wiegand, G. J. Sullivan, G. Bjontegaard, A. Luthra, Overview of the h.264/avc video coding standard. Circuits and Systems for Video Technology, IEEE Trans 13(7):560-576, 2003.
49. X. Wang, L. Ye, E. J. Keogh, C. R. Shelton, Annotating historical archives of images, JCDL, 341-350, 2008.
50. P. Wu, B. S. Manjunath, S. Newsam, H. D. Shin, A texture descriptor for browsing and similarity retrieval, Signal Processing: Image Communication, Volume 16, Issues 1-2, pages 33-43, September 2000.