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研究生:林文斌
研究生(外文):Lin, Wenbin
論文名稱(外文):Using Psychology Intention Diagram to Improve the Precision of Image Search Engine
指導教授:吳帆吳帆引用關係
指導教授(外文):Fan Wu
口試委員:吳帆胡雅涵蔡志豐王堯天
口試日期:2011.07.26
學位類別:碩士
校院名稱:國立中正大學
系所名稱:資訊管理學系暨研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:英文
論文頁數:37
中文關鍵詞:圖片搜尋心理意向語意擷取圖片分析
外文關鍵詞:image searchpsychology intentionsemantic retrievalimage analysis
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隨著網際網路上的圖片快速的增加,整個網路已逐漸成為一個圖片資料庫。然而,因為網路上的圖片繁多,以至於使用者無法輕易的搜尋到自己想要的圖片。而不像文字搜尋引擎,圖片搜尋引擎無法清楚地表達圖片在視覺上的意義。再者,因為一個關鍵字通常包含許多意思,因此很難透過一個使用者所輸入的關鍵字去搜尋去使用者所想要的圖片。為了要解決這些問題,本論文除了利用語意擷取和圖片分析以外,並會記錄使用者在搜尋圖片的過程,並以此來建立出使用者的心理意向圖,藉此去猜測使用者想要的圖片。除了使用者的心理意向圖,本論文亦建立網站設計者的心理意向圖來搜尋相關的圖片。而由實驗結果可知,本論文所提出的方法與圖片搜尋引擎擁有極高的準確度,可以輕易幫助使用者找到自己想要的圖片。
With increasing these images mushrooms on the Internet, the whole Internet is becoming an image database. However, there are so many images on the web that it is difficult to find desired images for users. Unlike the text search engine, the image search engine cannot fully recognize the visual meaning of the image. In addition, it is difficult to get the desired images from the keywords provided by the user, since a keyword may contain multiple meanings. To solve the problems, besides using the semantic retrieval and the image analysis, this paper also analyzes the images in the sequential probing of a user and constructs a psychological intention diagram of the user to guess what images the users want. Moreover, this paper also constructs the psychological intention diagram of the designer of the web pages containing the keyword. The experiment result shows that the proposed image search engine has high precision; therefore, the method of providing images can help users find the desired image easily.
Contents
摘要 i
Abstract ii
Contents iii
List of Tables iv
List of Figures v
1. Introduction 1
2. Literature Review 6
2.1 Image Analysis 6
2.2 Semantic Retrieval 7
2.3 User Intention 9
3. UISRC 13
3.1 Psychology Intention Diagram of page designer 13
3.2 Psychology Intention Diagram of user 17
3.2 Image Search in UISRC 22
4. Conclusions 29
References 3

List of Figures
Figure 1 The result of typing the keyword, “Apple”, in Google Images. 1
Figure 2 The three yellow images look different because of different material and texture 6
Figure 3 The images and their related information. The top of the figure is a web page containing these images. The bottom of the figure is the information about the images derived from the surrounding text, file name and website name 8
Figure 4 The image is to show the method of constructing the psychology intention diagram of web page designer 14
Figure 5 A set of search paths travelled by six users U1, U2, U3, U4, U5 and U6 The proposed image search engine uses the search paths to construct the PID of user 18
Figure 6 Image searching in the UISRC 28


Reference
[1] Barnard, K., Duygulu, P., & Forsyth, D. (2003). Recognition as Translating Images into Text. Internet Imaging IX, Electronic Imaging, 2003.
[2] Cai, D., He, X., Li, Z., Ma, W.-Y., & Wen, J.-R. (2004 ). Hierarchical Clustering of WWW Image Search Results Using Visual, Textual and Link Information. Paper presented at the Proceedings of the 12th annual ACM international conference on Multimedia New York, NY, USA.
[3] Carson, C., Belongie, S., Greenspan, H., & Malik, J. (2002). Blobworld: image segmentation using expectation-maximization and its application to image querying. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 24, 1026 - 1038
[4] Chang, C.-H., & Hsu, C.-C. (1998). Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval. Computer Networks and ISDN Systems, 30(1-7), 621-623.
[5 ]Charles F., Michael J S., and Vassilis A. (1996). Webseer: an Image Search Engine for the World Wide Web. Technical Report. University of Chicago, Chicago, IL, USA.
[6] Cheng, P.-C., Chien, B.-C., Ke, H.-R., & Yang, W.-P. (2008). A two-level relevance feedback mechanism for image retrieval. Expert Systems with Applications, 34(3), 2193-2200.
[7] Chien, L.-F. (1999). PAT-Tree-Based Adaptive Keyphrase Extraction for Intelligent Chinese Information Retrieval. Information Processing & Management, 35, 501-521
[8] Chuang, J. (2010). Clustering results of image searches by annotations and visual features. National Chung Cheng University, Chia-Yi, Taiwan.
[9] Coelho, T. A. S., Calado, P. P., Souza, L. V., Ribeiro-Neto, B., & Muntz, R. (2004). Image Retrieval Using Multiple Evidence Ranking. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 16, 408 - 417.
[10] Davison, B. D. (2000). Topical locality in the Web. Paper presented at the Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, New York, NY, USA.
[11] Diligenti, M., Coetzee, F., Lawrence, S., Giles, C. L., & Gori, M. (2000 ). Focused Crawling Using Context Graphs. Paper presented at the Proceeding VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases San Francisco, CA, USA.
[12] Ding, H., Liu, J., & Lu, H. (2008). Hierarchical Clustering-Based Navigation of Image Search Results Paper presented at the Proceeding of the 16th ACM
international conference on Multimedia, Vancouver, British Columbia, Canada.
[13] En C., Feng J., Mingjing L., Weiying M., Hai J. ( 2006). "Using Implicit
Relevane Feedback to Advance Web Image Search," IEEE International
Conference on Multimedia and Expo, ICME 2006, pp.1773-1776.
[14] Frankel, C., Swain, M. J., & Athitsos, V. (1996). WebSeer: An Image Search Engine for the World Wide Web.
[15] Goldberger, J., Gordon, S., & Greenspan, H. (2006). Unsupervised Image-Set Clustering Using an Information Theoretic Framework. IEEE TRANSACTIONS ON IMAGE PROCESSING, 15, 449 - 458
[16] Greenspan, H., Goldberger, J., & Ridel, L. (2001). A continuous probabilistic framework for image matching. Computer Vision and Image Understanding, 84, 384 - 406.
[17] Hsu, C.-C., & Wu, F. (2006). Topic-specific crawling on the Web with the measurements of the relevancy context graph. Information Systems 31.
[18] Hua, Z., Wang, C., Xie, X., Lu, H., & Ma, W.-Y. (2005). Automatic Annotation of Location Information for WWW Images. Paper presented at the Multimedia and Expo, 2005. ICME 2005. IEEE International Conference, Amsterdam
[19] Hua, Z., Wang, X.-J., Liu, Q., & Lu, H. (2005). Semantic Knowledge Extraction and Annotation for Web Images. Paper presented at the Proceedings of the 13th annual ACM international conference on Multimedia, New York, NY, USA.
[20] Jansen, B. J., Booth, D. L., & Spink, A. (2008). Determining the informational, navigational,and transactional intent of Web queries. Information Processing and Management, 44, 1251–1266.
[21] Jing, F., Wang, C., Yao, Y., Deng, K., Zhang, L., & Ma, W.-Y. (2006). IGroup: Web Image Search Results Clustering. Paper presented at the Proceedings of the 14th annual ACM international conference on Multimedia.
[22] Jung, S., Herlocker, J. L., & Webster, J. (2007). Click data as implicit relevance feedback in web search. Information Processing & Management, 43(3), 791-807.
[23] Kherfi, M. L., Ziou, D., & Bernardi, A. (2003). Combining positive and negative examples in relevance feedback for content-based image retrieval. Journal of Visual Communication and Image Representation, 14(4), 428-457.
[24] Lux, M., Kofler, C., & Marques, O. (2010). A classification scheme for user intentions in image search. Paper presented at the Proceedings of the 28th of the international conference extended abstracts on Human factors in computing systems, New York, NY, USA.
[25] Stephen, A. T., & Toubia, O. (2009). Explaining the power-law degree distribution in a social commerce network. Social Networks, 31(4), 262-270.
[26] Pan, J.-Y., Yang, H.-J., Faloutsos, C., & Duygulu, P. (2004 ). GCap: Graph-based Automatic Image Captioning. Paper presented at the Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Washington, DC.
[27] Wang, S., Jing, F., He, J., Du, Q., & Zhang, L. (2007). IGroup: Presenting Web Image Search Results in Semantic Clusters. Paper presented at the Proceedings of the SIGCHI conference on Human factors in computing systems, San Jose, California, USA.
[28] Wu, F., Chiu, I.-H., & Lin, J.-R. (2005). Prediction of the Intention of Purchase of the User Surfing on the Web Using Hidden Markov Model Paper presented at the Proceedings of ICSSSM '05. 2005 International Conference on Services Systems and Services Management, Chongquing, China
[29] Yu, S., Cai, D., Wen, J.-R., & Ma, W.-Y. (2003). Improving Pseudo-Relevance Feedback in Web Information Retrieval Using Web Page Segmentation. Paper presented at the Proceedings of the 12th international conference on World Wide Web New York, NY, USA.
[30] Zeng, H.-J., He, Q.-C., Chen, Z., Ma, W.-Y., & Ma, J. (2004). Learning to cluster web search results. Paper presented at the Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, New York, NY, USA.
[31] Google Images, (2011). Retrieved May 5, 2011, from http://www.google.com/imghp
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