政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/32650

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/32650

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文笔数/总笔数 : 113318/144297 (79%)
造访人次 : 51064654 在线人数 : 972

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

搜寻范围

查询小技巧：

您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果

若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据

进阶搜寻

主页 ‧ 登入 ‧ 上传 ‧ 说明 ‧ 关于政大典藏 ‧ 管理

到手机版

政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 > Item 140.119/32650

请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/32650

题名:	根據概念學習發展以內容為主的音樂查詢之相關回饋機制 Relevance feedback for content-based music retrieval based on semantic concept learning
作者:	江孟芬 Chiang, Meng-Fen
贡献者:	沈錳坤 Shan, Man-Kwan 江孟芬 Chiang, Meng-Fen
关键词:	資料探勘音樂檢索相關回饋 Data Mining Music retrieval Relevance Feedback
日期:	2006
上传时间:	2009-09-17 13:56:24 (UTC+8)
摘要:	傳統的音樂檢索系統主要在提供使用者特定音樂的查詢(target search)。除此之外，使用者也有類型音樂查詢(category search)的需求。在類型音樂查詢中，該類型的所有音都共同具備使用者所定義的概念(semantic concept)。這個由使用者定義的概念在音樂檢索系統上是主觀的且動態產生的。換句話說，同一使用者在不同情境之下對於同一首音樂可能產生不同的解讀概念。為了動態擷取使用者的概念，讓使用者參與在查詢過程的互動機制是必要的。因此，我們提出將相關回饋(relevance feedback)的機制運用在以內容為主的音樂查詢系統上，讓系統從使用者的相關回饋中學習使用者的概念，並利用這學習出的概念來幫助音樂查詢。由於使用者可能從整首音樂或音樂片段兩種角度來判斷該音樂是否具備使用者定義的概念。因此，本論文提出用以片段為主的音樂模型(segment-based modeling approach)將音樂表示成音樂片段的集合。進一步再從整首音樂和片段中擷取特徵。其次，我們針對該問題提出相關演算法來探勘使用者的概念。該演算法先從相關和不相關的音樂資料庫中個別探勘常見樣式，再利用這些樣式建立分類器以區分音樂的相關性。最後，我們分析各種系統回饋機制對搜尋效果的影響。Most-positive回傳機制會選擇根據目前系統判斷為最相關的物件。Most-informative機制則是回傳系統無法判斷其相關性的音樂物件。Most-informative 機制的目的在增加每回合系統從使用者身上得到的資訊量。Hybrid 則是中和前兩種機制的優點。本文中，我們模擬並比較各種回傳機制的效能。實驗結果顯示相關回饋機制確實能提升查詢的效果。 Traditional content-based music retrieval system retrieves a specific music object which is similar to the user’s query. There is also a need, category search, for retrieving a specific category of music objects. In category search, music objects of the same category share a common semantic concept which is defined by the user. The concept for category search in music retrieval is subjective and dynamic. Different users at different time may have different interpretations for the same music object. In the music retrieval system along with relevance feedback mechanism, users are expected to be involved in the concept learning process. Relevance feedback enables the system to learn user’s concept dynamically. In this paper, the relevance feedback mechanism for category search of music retrieval based on the semantic concept learning is investigated. We proposed a segment-based music representation to assist the system in discovering user’s concept in terms of low-level music features. Each music object is modeled as a set of significant motivic patterns (SMP) achieved by discovering motivic repeating pattern. Both global and local music features are considered in concept learning. Moreover, to discover user’s semantic concept, a two-phase frequent pattern mining algorithm is proposed to discover common properties from relevant and irrelevant objects respectively and based on which a classifier is derived for distinguishing music objects. Except user’s feedback, three strategies of the system’s feedback to select objects for user’s relevance judgment are investigated. Most-positive strategy returns the most relevant music object to the user while most-informative strategy returns the most uncertain music objects for improving the discrimination power of the next round. Hybrid feedback strategy returns both of them. Comparative experiments are conducted to evaluate effectiveness of the proposed relevance feedback mechanism. Experimental results show that a better precision can be achieved via proposed relevance feedback mechanism.
參考文獻:	[1] Agrawal, R. and Srikant, R. Fast algorithms for mining association rules. In Proc. of Intl. Conference on Very Large databases (VLDB ‘94), (Chile, September 12-15, 1994). [2] Amir, A. Berg, M. and Permuter. H. Mutual relevance feedback for multimodal query formulation in video retrieval. In Proc. of ACM SIGMM Intl. Workshop on Multimedia Information Retrieval (MIR ‘05) (Singapore, November 10-11, 2005). [3] Chen, H. and Chen Arbee L.P. A music recommendation system based on music data grouping and user interests. In Proc. of the ACM CIKM Intl. Conference on Information and Knowledge Management (CIKM ‘01) (Atlanta, Georgia, USA, November 5-10, 2001). ACM press 2001, 231-238. [4] Cox, I.J. Miller, M. Minka, T.P. Papathomas, T. and Yianilos P. The baysian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments. IEEE Trans. Image Processing, 9, 1, (Jan. 2000), 20-37. [5] Grimaldi, M. and Cunningham. P. Experimenting with music taste prediction by user profiling. In Proc. of ACM SIGMM Intl. Workshop on Multimedia Information Retrieval (MIR ‘04) (New York, NY, USA, October 15-16, 2004). ACM press 2004, 173-180. [6] Haas, M. Lamel, L. Thomee, B. and Lew, M.S. Relevance feedback: perceptual learning and retrieval in bio-computing, photos, and video. In Proc. of the ACM SIGMM Intl. Workshop on Multimedia Information Retrieval (MIR ‘04) (New York, NY, USA, October 15-16, 2004). ACM press 2004, 151-156. [7] Han, E.H. and Karpis, G. Feature-based Recommendation System. In Proc. of the ACM CIKM Intl. Conference on Information and Knowledge Management (CIKM ‘05) (Bremen, Germany, October 31-November 5, 2005). ACM press 2005, 446-452. [8] He, X.F. King, W.Y. Ma, W.Y. Li, M.J. and Jiang, H.J. Learning a semantic space from user’s relevance feedback for image retrieval. IEEE Trans. Circuits and Systems and Video Technology, 13, 1 (Jan 2003), 39-48. [9] Ho, M.C. Theme-Based Music Structural Analysis. Master Thesis, University of Chen Chi, Taipei, Taiwan, 2004. [10] Hoi, C.H. and Lyu, M.R. A novel log-based relevance feedback technique in content-based image retrieval. In Proc. of the ACM Intl. Conference on Multimedia (MM ‘04) (New York, NY, USA, October 10-16, 2004). ACM press 2004, 24-31. [11] Hoashi, K. Matsumoto, K. and Inoue, N. Personalization of user profiles for content-based music retrieval based on relevance feedback. In Proc. of the ACM Intl. Conference on Multimedia (MM ‘03) (Berkeley, CA, USA, November 2-8, 2003). ACM press 2003, 110-119. [12] Hsu, J.L. Liu, C.C. and Chen, Arbee L.P. Discovering non-trivial repeating patterns in music data. In Proc. of Intl. Conference on Data Engineering (ICDE ‘99) (Sydney, Australia, March 23-36, 1999). IEEE Computer Society Press, 1999, 14-21. [13] Jing, F. Li, M. Zhang, L. Zhang, H.J. and Zhang, B. Relevance feedback in region-based image retrieval. IEEE Trans. Circuits and Systems and Video Technology, 14, 5 (May 2004), 672-681. [14] Jing, F. Li, M. Zhang, H.J. Zhang, B. An effective region-based image retrieval framework. In Proc. of the ACM Intl. Conference on Multimedia (MM ‘02) (Juan les Pins, France, December 1-6, 2002). ACM press 2002, 456-465. [15] Kuo, F.F. and Shan, M.K. A personalized music filtering system based on melody style classification. In Proc. of the IEEE Intl. Conference on Data Mining (ICDM ‘02) (Maebashi, Japan, December 9-12, 2002). IEEE Computer Society Press 2002, 649-652. [16] Liu, B. Hsu, W. and Ma, Y. Integrating Classification and Association Rule Mining. In Proc. of the Intl. Conference on Knowledge Discovery and Data Mining (KDD’98) (New York, USA, August 27-31, 1998). AAAI Press, 1998, 80-86. [17] Liu, C.C. Hsu, J.L. and Chen, A.L.P. An approximating string matching algorithm for content-based music data retrieval. In Proc. of IEEE Intl. Conference on Multimedia Computing and Systems (ICMCS ‘99) (Florence, Italy, June 7-11, 1999). IEEE Computer Society, Press 1999, 451-456. [18] Ortega-Binderberger, and M. Mehrotra, S. Relevance feedback techniques in the MARS image retrieval system. Multimedia System, 9, 6 (June. 2004)535-547. [19] Ragno, R. Burges, C.J.C. and Herley, C. Inferring similarity between music objects with application to playlist generation. In Proc. of ACM SIGMM Intl. Workshop on Multimedia Information Retrieval (MIR ‘05) (Singapore, November 10-11, 2005). [20] Rocchio, J.J. and G. Salton. Relevance feedback in information retrieval. Prentice Hall. Inc., Englewood Cliffs, New Jersey, 1971. [21] Rui, Y. Huang, Thomas S. Ortega, M. and Mehrotra, S. Relevance feedback: a power tool for interactive content-based image retrieval. IEEE Trans. Circuits and Systems for Video Technology, 8, 5 (Sep. 1998), 644-655. [22] Schohn, G. and Cohn, D. Less is more: active learning with support vector machines In Proc. of the Intl. Conference on Machine Learning (ICML ‘00) (Stanford, CA, USA, June 29-July 2, 2000). Morgan Kaufmann 2000, 839-846. [23] Selfridge-Field. E. Conceptual and representational issues in melodic comparison. In Hewlett, W.B. & Selfridge-Field E. Melodic similarity: Concepts, procedures, and applications (Computing in Musicology: 11), The MIT Press. [24] Shardanand, U. and Maes, P. Social information filtering: algorithms for automating “Word of Mouth” In Proc. of the Conference on Human Factors in Computing Systems (CHI ‘95) (Denver, Colorado, USA, May 7-11, 1995). ACM/Addison-Wesley Press 1995, 210-217. [25] Shen, X. and Zhai, C. Active feedback in Ad Hoc information retrieval. In Proc. of ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ‘05) (Salvador, Bahia, Brazil, August 15-19, 2005). ACM press 2005, 59-66. [26] Stein, L. Structure & Style. Summy-Birchard, 1979. [27] Tong, S. and Koller, D. Support vector machine active learning with applications to text classification. Journal of Machine Learning Research, 2 (Nov. 2001), 45-66. [28] Tong, S. and Chang, E. Support vector machine active learning for image retrieval. In Proc. of ACM Intl. conference on Multimedia (MM ‘01) (Ottawa, Ontario, Canada, September 30- October 5, 2001). ACM press 2001, 107-118. [29] Uitdenbgerd, A. and Zobel, J. Melodic matching techniques for large music databases. In Proc. of ACM Intl. conference on Multimedia (MM ‘99) (Orlando, Florida, USA, October 30-Novermber 5, 1999). ACM press 1999, 57-66. [30] Wu, Y. and Zhang, A. Interactive pattern analysis for relevance feedback in multimedia information retrieval. Multimedia System, 10, 1 (June. 2004)41-55. [31] Wu, Y. Tian, Q. and Huang, T.S. Discriminant-EM algorithm with application to image retrieval. In Proc. of IEEE Intl. Conference on Computer Vision and Pattern Recognition (CVPR ‘00) (Hilton Head, SC, USA, June 13-15, 2000). IEEE computer society 2000, 1222-1227. [32] Yan, R. Hauptmann. A. and Jin, R. Negative pseudo-relevance feedback in content-based video retrieval. In Proc. of the ACM Intl. Conference on Multimedia (MM ‘03) (Berkeley, CA, USA, November 2-8, 2003). ACM press 2003, 343-346. [33] Zhou, X.S. and Huang, Thomas S. Relevance feedback in image retrieval: a comprehensive review. Multimedia System, 8, 6 (Apr. 2003)536-544.
描述:	碩士國立政治大學資訊科學學系 93753009 95
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0093753009
数据类型:	thesis
显示于类别:	[資訊科學系] 學位論文

文件中的档案:

档案	描述	大小	格式	浏览次数
75300901.pdf		46Kb	Adobe PDF2	788	检视/开启
75300902.pdf		62Kb	Adobe PDF2	769	检视/开启
75300903.pdf		72Kb	Adobe PDF2	897	检视/开启
75300904.pdf		18Kb	Adobe PDF2	719	检视/开启
75300905.pdf		23Kb	Adobe PDF2	815	检视/开启
75300906.pdf		30Kb	Adobe PDF2	856	检视/开启
75300907.pdf		56Kb	Adobe PDF2	935	检视/开启
75300908.pdf		156Kb	Adobe PDF2	845	检视/开启
75300909.pdf		64Kb	Adobe PDF2	771	检视/开启
75300910.pdf		14Kb	Adobe PDF2	716	检视/开启
75300911.pdf		21Kb	Adobe PDF2	793	检视/开启

在政大典藏中所有的数据项都受到原著作权保护.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - 回馈