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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳銘憲(Ming-Syan Chen) | |
| dc.contributor.author | Yi-Ling Chen | en |
| dc.contributor.author | 陳奕伶 | zh_TW |
| dc.date.accessioned | 2021-06-13T16:33:06Z | - |
| dc.date.available | 2005-07-28 | |
| dc.date.copyright | 2005-07-28 | |
| dc.date.issued | 2005 | |
| dc.date.submitted | 2005-07-10 | |
| dc.identifier.citation | [1] R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In Proc. of Int’l Conf. on Very Large Data Bases, pages 487–499, Sep. 1994.
[2] A. G. Buchner and M. Mulvenna. Discovery internet marketing intelligence through online analytical web usage mining. Proc. of ACM SIGMOD, 27(4):54–61, Dec. 1998. [3] L. Chen, S. S. Bhowmick, and L.-T. Chia. Vrules: An effective association-based classifier for videos. In ACM Int’l Workshop on Multimedia Databases month =. [4] M.-S. Chen, J. Han, and P. S. Yu. Data mining: An overview from database perspective. IEEE Transactions on Knowledge and Data Engineering, 5(1):866–883, Dec. 1996. [5] W.-T. Chen, Y.-L. Chen, and M.-S. Chen. Mining frequent spatial patterns in image databases with applications to image classification. In Submission of Int’l Conf. on Data Engineering, Jun. 2005. [6] Y. C. Cheng and S. Y. Chen. Image classification using color, texture and regions. Image and Vision Computing, 21:759–776, 2003. [7] T.-S. Chua, K.-L. Tan, and B. C. Ooi. Fast signature-based color-spatial image retrieval. In ICMCS, pages 362–369, 1997. [8] U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurasamy. Advances in Knowledge Discovery and Data Mining. MIT Press, Cambridge, MA, 1996. [9] Y. Gong, H. Chua, and X. Guo. Image indexing and retrieval based on color histogram. In Int’l Conf. on Multimedia Modeling, pages 115–126, 1995. [10] J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2000. [11] W. Hsu, J. Dai, and M. L. Lee. Mining viewpoint patterns in image databases. In Proc. of ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining, pages 553–558, 2003. [12] J. Huang, S. R. Kumar, and R. Zabih. An Automatic Hierarchical Image Classification Scheme. In Proc. of ACM Multimedia, pages 219–228, 1998. [13] B. Liu, W. Hsu, and Y. Ma. Integrating classification and association rule mining. In KDD, pages 80–86, 1998. [14] C. Ordonez and E. Omiecinski. Discovering association rules based on image content. In Proc. of the IEEE Advances in Digital Libraries Conference, 1999. [15] J. S. Park, M.-S. Chen, and P. S. Yu. Using a hash-based method with transaction trimming for mining association rules. IEEE Transactions on Knowledge and Data Engineering, 9(5):813–825, 1997. [16] Y. Rubner, C. Tomasi, and L. J. Guibas. A metric for distributions with applications to image databases. In Int’l Conf. on Computer Vision, 1998. [17] Y. Rui, T. S. Huang, and S. F. Chang. Image Retrieval: current techniques, promising directions, and open issues. Journal of Visual Communication and Image Representation, 10(1), Mar. 1999. [18] A. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain. Content-based image retrieval at the end of the early years. IEEE Trans. on Pattern Anal.and Machine Intell., 22(12):1349–1380, Dec. 2000. [19] J. R. Smith. Integrated spatial and feature image systems: Retrieval, analysis and compression. In Ph. D. thesis, 1997. [20] M. Szummer and R. Picard. Indoor-outdoor image classification. IEEE Int’l Workshop on Content-based Access of Images and Video Databases, pages 42–51, 1998. [21] J. Tesic, S. Newsam, and B. S. Manjunath. Mining image datasets using perceptual association rules. In Int’l Conf. of SIAM on Mining Scientific and Engineering Datasets, May 2003. [22] A. Vailaya, M. A. T. Figueiredo, A. K. Jain, and H.-J. Zhang. Image classification for content-based indexing. IEEE Trans. on Image Processing, 10(1):117–130, Jan. 2001. [23] Z. Yang and C.-C. J. Kuo. Survey on Image Content Analysis, Indexing, and Retrieval Techniques and Status Report of MPEG-7. Tamkang Journal of Science and Engineering, 2(3):101–118, 1999. [24] O. R. Zaiane and J. Han. Multimediaminer: A system prototype for multimedia data mining. In Proc. of ACM SIGMOD, 1998. [25] O. R. Zaiane, J. Han, and H. Zhu. Mining recurrent items in multimedia with progressive resolution refinement. In Proc. of Int’l Conf. on Data Engineering, page 461, 2000. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/38422 | - |
| dc.description.abstract | 在這篇論文中，我們結合了資料探戡和影像處理技術，協助我們找出影像中所隱藏的空間關係與資訊。為了找出這些資訊，我們提出一個影像探戡的架構：空間關聯法則，所謂空間關聯法則是用來描述影像中某個位置的內容與另一個位置的內容存有關聯性，我們提供了演算法來找出這些空間關聯法則。在應用方面，我們將空間關聯法則實作在影像分類上。我們找出自然景物照片中色彩的空間關連性，例用這些關聯法則來做新影像的分類。論文中的實驗結果可以顯示，利用色彩空間關聯法則來分類，準確度可以達到86%。 | zh_TW |
| dc.description.abstract | In this paper, we integrate data mining with image processing for discovering spatial relationships in images. We present an image mining framework, Spatial Association Rulemining (SAR), to mine spatial associations located in specific locations of images. A rule in the SAR refers to the occurrences of image content in a pair of spatial locations. The proposed approach is applied to mine color spatial association rules (color-SAR) in landscape scene images so as to demonstrate that the spatial association rules is able to the application of image classification. Our experimental results show that the classification accuracy of 86% can be achieved by the rule-based classifier. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T16:33:06Z (GMT). No. of bitstreams: 1 ntu-94-R92921028-1.pdf: 332029 bytes, checksum: 1d08a280922860e555e01120f1495202 (MD5) Previous issue date: 2005 | en |
| dc.description.tableofcontents | 1 Introduction 6
2 RelatedWork 12 3 Preliminaries 14 3.1 Algorithm Apriori 14 3.2 The Earth Mover's Distance 17 4 Spatial Association Rule 19 4.1 Problem Formulation 19 4.2 Algorithm SAR-Mining 21 5 Application to Image Classification 28 5.1 Extraction of color items 29 5.2 Generation of color-SAR in each class 30 5.3 The image classifier 31 5.3.1 Distance Measurement 31 5.3.2 Classification of An Image 34 6 Experimental Results 37 6.1 Rule-based classifier 37 7 Conclusion 43 | |
| dc.language.iso | en | |
| dc.subject | 關連法則 | zh_TW |
| dc.subject | 影像 | zh_TW |
| dc.subject | image | en |
| dc.subject | association rule | en |
| dc.title | 影像中的空間關聯法則 | zh_TW |
| dc.title | Mining Spatial Association Rules in Image | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 93-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 呂永和(Yung-Ho Leu),沈錳坤(Man-Kwan Shan) | |
| dc.subject.keyword | 關連法則,影像, | zh_TW |
| dc.subject.keyword | association rule,image, | en |
| dc.relation.page | 44 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2005-07-11 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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