請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/35966完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李瑞庭 | |
| dc.contributor.author | Songkai Su | en |
| dc.contributor.author | 蘇頌凱 | zh_TW |
| dc.date.accessioned | 2021-06-13T07:48:53Z | - |
| dc.date.available | 2005-07-30 | |
| dc.date.copyright | 2005-07-30 | |
| dc.date.issued | 2005 | |
| dc.date.submitted | 2005-07-26 | |
| dc.identifier.citation | [1] R. Agrawal and R. Srikant, “Fast algorithms for mining association rules”, In Proceeding of International Conference Very Large Data Bases (VLDB’94), pp.487-499, Sept. 1994.
[2] A. R. Ahmadyfard and J. Kittler, “Using relaxation technique for region-based object recognition”, Image and Vision Computing, Vol. 20, pp. 769-781, 2002. [3] G. Chen, Q. Wei, D. Liu and G. Wets, “Simple association rules (SAR) and the SAR-based rule discovery”, Computers and Industrial Engineering, Vol. 43, pp.721-733, 2002. [4] Y. L. Chen, S. S. Chen and P. Y Hsu, “Mining hybrid sequential patterns and sequential rules”, Information Systems, Vol. 27, pp. 345-362, 2002. [5] Y. L. Chen, M. C. Chiang and M. T. Ko, “Discovering time-interval sequential patterns in sequence databases”, Expert Systems with Applications, Vol. 25, pp.343-354, 2003. [6] T. D’Orazio, C. Guaragnella and M. Leo, A. Distante, “A new algorithm for ball recognition using circle Hough transform and neural classifier”, Pattern Recognition, Vol. 37, pp.393-408, 2004. [7] D. Gusfield, Algorithms on strings, trees, and sequences, the press syndicate of the University of Cambridge, pp.287-291, 1997. [8] J. Han and M. Kamber, Data mining concepts and techniques, Morgan Kaufmann Publishers, San Francisco, CA, USA, pp.230-236, 2001. [9] E. R. Hancock and J. Kittler, “Discrete relaxation”, Pattern Recognition, pp.711-733, 1990. [10] W. Hsu, J. Dai and M. L. Lee, “Mining viewpoint patterns in image databases”, in Proceeding 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 2003. [11] Y. L. Lee and R. H. Park, “A surface-based approach to 3-D object recognition using a mean field annealing neural network”, Pattern Recognition, Vol. 35, pp.299-316, 2002. [12] B. Leibe and B. Schiele, “Analyzing appearance and contour based methods for object categorization”, in Proceeding of International Conference on Computer Vision and Pattern Recognition, Madison, USA, June 2003. [13] J. Li and L. Wong, “Identifying good diagnostic gene groups from gene expression profiles using the concept of emerging patterns”, Bioinformatics, pp.725-734, 2002. [14] J. Li, H. Shen and R. Topor, “Mining the optimal class association rule set”, Knowledge-based Systems, Vol. 15, pp.399-405, 2002. [15] W. J. Li and T. Lee, “Object recognition and articulated object learning by accumulative Hopfield matching”, Pattern Recognition, Vol. 35, pp. 1933-1948, 2002. [16] S. Lu, Z. Wang and J. Shen, “Neuro-fuzzy synergism to the intelligent system for edge detection and enhancement“, Pattern Recognition, Vol. 36, pp.2395-2409, 2003. [17] B. F. Park, K. M. Lee, S. U. Lee and J. H. Lee, “Recognition of partially occluded objects using probabilistic ARG (attributed relational graph)-based matching”, Computer Vision and Image Understanding, Vol. 90, pp.217-241, 2003. [18] E. G. M. Petrakis, C. Faloutsos and K. I. Lin, “ImageMap: an image indexing method based on spatial similarity”, IEEE Transactions on Knowledge and Data Engineering, Vol. 14, No.5, pp.979-987, September/October 2002. [19] R. Rastogi and K. Shim, “Mining optimized support rules for numeric attributes”, Information Systems, Vol. 26, pp.425-444, 2001. [20] E. Rivas and S. R. Eddy, “A dynamic programming algorithm for RNA structure prediction including pseudoknots”, Academic Press, Vol. 285, pp.2053-2068, 1999. [21] J. A. Rushing, H. Ranganath, T. H. Hinke and S. J. Graves, “Image segmentation using association rule features”, IEEE Trans. on Image Processing, Vol. 11, No.5, pp.558-567, May 2002. [22] D. Sanchez, J. Chamorro-Martinez and M. A. Vila, “Modelling subjectivity in visual perception of orientation for image retrieval”, Information Processing and Management, Vol. 39, pp.251-266, 2003. [23] B. S. Song and K. M. Lee, “Model-based object recognition using geometric invariants of points and lines”, Computer Vision and Image Understanding, Vol. 84, pp.361-383, 2001. [24] D. G. Sim, Y. K. Ham and R.H. Park, “On-line recognition of cursive Korean characters using DP matching and fuzzy concept”, Pattern Recognition, Vol.27, No. 12, pp.1605-1620, 1994. [25] C. de Trazegnies, C. Urdiales, A. Bandera and F. Sandoval, “3D object recognition based on curvature information of planar views”, Pattern Recognition, Vol. 36, pp.2571-2584, 2003. [26] K. Wang, Y. He and J. Han, “Pushing support constraints into association rules mining”, IEEE Transactions on Knowledge and Data Engineering, Vol. 15, No.3, pp.642-658, 2003. [27] A. Wong and M. You, “Entropy and distance of random graphs with application to structural pattern recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, pp.509-609, 1985. [28] J. Zhang, X. Zhang, H. Krim and G. G. Walter, “Object representation and recognition in shape spaces”, Pattern Recognition, Vol. 36, pp.1143-1154, 2003. [29] D. Lopresti and J. Zhou, “Retrieval strategies for noisy text”, in Proceedings of the Fifth Annual Symposium on Document Analysis and Information Retrieval, April 15-17, pp. 255-269, 1996. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/35966 | - |
| dc.description.abstract | 物件辨識的目的是識別圖片中的物件並且將物件歸類到具有相同特性的類別中，本篇論文中我們利用資料探勘方法找出特徵樣式達成物件辨識的目的，方法主要分成三個階段。第一，從物件的輪廓中找出高曲率點並且將兩點間的距離以及該點的曲率型成可以表示物件的序列；第二階段，利用Apriori演算法找出最大頻繁樣式(maximal frequent pattern)作為各種類別物件的特徵樣式(feature pattern)，這個階段引用模糊觀念因此可以接受物件少許的變化；最後利用近似序列比對演算法(approximate sequential matching)比對物件，這個演算法屬於動態規劃問題，如果特徵樣式中的項目不完全符合於序列中且不符合的數量不大於使用者事前定義的門檻值，這個特徵樣式就符合該條序列，因此該條序列所代表的物件應該被歸類到該特徵樣式代表的類別。
實驗結果顯示，我們提出的方法效果比使用地標的方法好而且不需要很多訓練資料就可以辨識出具有相似姿勢的物件。 | zh_TW |
| dc.description.abstract | The goal of object recognition is to identify the object in an image and objects are classified into several categories which shares a common feature. In this thesis, we proposed a data mining approach consisting of three phases to realize object recognition by a means of mining feature patterns. First, high curvature points are extracted from the contour of an object and the distances between two points and the curvatures at these points are recoded to form a sequence to represent the shape of this object. In the second phase, Apriori algorithm is used to discover the maximal frequent patterns as feature patterns of every category. A fuzzy concept is included into the process of mining feature patterns to tolerance slight transformations. Finally, matching is done by approximate sequential matching which is a dynamic programming problem. If the number of mismatch items between a feature pattern and a sequence is no more than a user-defined threshold, we say the feature pattern is matched with the sequence. Thus, the object of this sequence is classified into the category of this feature pattern.
Experimental results show that our method performs better than a landmark-based method and does not require many training dataset to identify objects with similar poses. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T07:48:53Z (GMT). No. of bitstreams: 1 ntu-94-R92725015-1.pdf: 381992 bytes, checksum: a492f57b3cf804460226c8bf62a40098 (MD5) Previous issue date: 2005 | en |
| dc.description.tableofcontents | Chapter 1 Introduction 1
Chapter 2 Literature Survey 3 Chapter 3 Attributed Relational Graphs 8 Chapter 4 Feature Patterns Mining 11 4.1 Basic concept 11 4.2 Sequential pattern mining for image data 12 4.3 Mining feature patterns from images 12 Chapter 5 Matching Algorithm 17 Chapter 6 Experiment Design and Performance Evaluation 19 6.1 Experiment design 19 6.2 Performance evaluation 20 References 24 | |
| dc.language.iso | en | |
| dc.subject | 序列相似性 | zh_TW |
| dc.subject | 物件辨識 | zh_TW |
| dc.subject | 序列模式探勘 | zh_TW |
| dc.subject | sequential pattern mining | en |
| dc.subject | sequence similarity | en |
| dc.subject | object recognition | en |
| dc.title | 利用資料探勘方法辨識物件 | zh_TW |
| dc.title | A Data Mining Approach to Object recognition | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 93-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 傅楸善,陳良華 | |
| dc.subject.keyword | 物件辨識,序列模式探勘,序列相似性, | zh_TW |
| dc.subject.keyword | object recognition,sequential pattern mining,sequence similarity, | en |
| dc.relation.page | 27 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2005-07-26 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-94-1.pdf 未授權公開取用 | 373.04 kB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。