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The concept of utilizing association rules for classification has emerged in recent years. This approach has often proved to be more efficient and accurate than traditional techniques. In this paper we extend the existing associative classifier building algorithms and apply them to the problem of image classification. We describe a set of photographs with features calculated on the basis of their color and texture characteristics and experiment with different types of rules which use the information about the existence of a particular feature in an image, its occurrence count and spatial proximity to classify the images accurately. We suggest using association rules more closely tied to the nature of the image data and compare the results with those of classification with simpler rules, taking into consideration only the existence of a particular feature on an image.
Słowa kluczowe
Wydawca
Rocznik
Tom
Strony
35--45
Opis fizyczny
Bibliogr. 15 poz., rys., tab.
Twórcy
autor
autor
- Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland, K.Walczak@ii.pw.edu.pl
Bibliografia
- [1] Kobyliński L and Walczak K 2006 Proc. Int. IIS: Intelligent Information Processing and Web Mining Conf. Advances in Soft Computing, Ustron, Poland, pp. 479-487
- [2] Bayardo R 1997 Proc. 3rd Int. Conf. on Knowledge Discovery and Data Mining, Newport Beach, CA, USA, pp. 123-126
- [3] Liu B, Hsu W and Ma Y 1998 Proc. 4th Int. Conf. on Knowledge Discovery and Data Mining, New York, USA, pp. 80-86
- [4] Li W, Han J and Pei J 2001 IEEE Int. Conf. on Data Mining, Washington, D.C., USA, pp. 369-376
- [5] Zaiane O R and Antonie M-L 2002 Proc. 13th Australasian Database Conference, Melbourne, Australia, pp. 215-222
- [6] Zaiane O R, Han J and Zhu H 2000 Proc. 16th Int. Conf. on Data Eng., San Diego, USA, pp. 461-470
- [7] Ong K-L, Ng W-K and Lim E-P 2001 Proc. 3rd Int. Conf. on Information, Communications 8 Signal Processing (ICICS'2001), Singapore, 5 pages
- [8] Rak R, Stach W, Zaiane and Antonie M-L 2005 Proc. 9th Pacific-Asia Conf. on Knowledge Discovery and Data Mining, Hanoi, Vietnam, pp. 240-248
- [9] Antonie M-L, Zaiane 0 Rand Coman A 2002 Revised Papers from MDM/KDD and PAKDD/KDMGD, pp. 68-83
- [10] Ardizzone E, Damel T, Maniscalco U and Rigotti C 2001 Proc. 1st Int. Workshop on Multimedia Data and Document Eng., Lyon, France, 6 pages
- [11] Tešić J, Newsam Sand Manjunath B S 2003 Proc. SIAM Int. Conf. on Data Mining, 6th Workshop on Mining Scientific and Engineering Datasets, San Francisco, USA, pp. 71-77
- [12] Zhu L, Rao A and Zh ang A 2002 AGM Trans. Inf. Syst. 20 224
- [13] Manjunath B S and Ma W 1996 IEEE Trans. on Patt. Anal. and Machine Intell. 18 837
- [14] Ma W Y and Manjunath B S 2000 IEEE Trans. on Image Processing 9 1375
- [15] Wang J Z, Li J and Wiederhold G 2001 IEEE Trans. on Patt. Anal. and Machine Intell. 23 947
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPG4-0035-0048