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Li Kar Wing. / "July 2004." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (p. 127-139). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

In this thesis, we first investigate how to improve user experience of browsing large photos on mobile devices. Currently, the predominant methods for accessing large photos on small devices are down-sampling or manual browsing by zooming and scrolling. Image down-sampling or thumbnail view results in significant information loss due to the excessive resolution reduction. Manual browsing can avoid information loss but it is often time-consuming for users to catch the most crucial information of an image. Employing the concept of information asymmetry, the thesis investigates an image attention model and its extensions under different application scenarios. The image attention model can successfully solve the small screen browsing problem. First of all, different parts of an image contain different information, computer vision technology can analysis image and extract the attention objects to build the image attention model. Secondly, browsing large photo on small display can be formulated as a problem of optimized manipulating of attention objects to improve viewer's browsing experience, which brings about new functions such as automatic browsing and interactive browsing. Thirdly, the screen limitations of the mobile devices will force users to scroll and zoom while browsing images, which can be regarded as direct indications for users' attention, therefore new attention objects can be discovered by analyzing the past browsing log. Lastly, since psycho-physiological research tells us that the importance of an attention object may vary with the users' preferences and tasks, so attention model should also be adaptive to the users' interest. / The thesis also investigates the problem on how to improve the usability of the current image search engines, that is, how to explore the image search results on desktop PCs and mobile devices in a more effective way. The research results show that unsupervised learning technology can help improve the browsing efficiency. The similarity-based presentation method and clustering-base method are better for desktop PC platform and mobile platform respectively. The navigation operations are re-designed to make the new interface easy to use. Based on these results, the thesis also tries to investigate the problem on how to incorporate users' interaction in content-based image retrieval relevance feedback process. The new approach employs the parameter embedding visualization, multi-classifier learning and fusion methods to provide a seamless integrated browsing, user feedback and classifier learning process. (Abstract shortened by UMI.) / Liu Hao. / "July 2005." / Adviser: Xiaoou Tang. / Source: Dissertation Abstracts International, Volume: 67-07, Section: B, page: 3907. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (p. 107-115). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract in English and Chinese. / School code: 1307.

Sha Lifeng. / "December 2004." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (p. 115-122) / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

Pun Chi Man. / "July 2002." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (p. 119-127). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

by Donald Asogu Adjeroh. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (p. 202-216). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web.

Lai Kwok-yin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 125-130). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Automatic Textual Document Categorization --- p.1 / Chapter 1.2 --- Meta-Learning Approach For Text Categorization --- p.3 / Chapter 1.3 --- Contributions --- p.6 / Chapter 1.4 --- Organization of the Thesis --- p.7 / Chapter 2 --- Related Work --- p.9 / Chapter 2.1 --- Existing Automatic Document Categorization Approaches --- p.9 / Chapter 2.2 --- Existing Meta-Learning Approaches For Information Retrieval --- p.14 / Chapter 2.3 --- Our Meta-Learning Approaches --- p.20 / Chapter 3 --- Document Pre-Processing --- p.22 / Chapter 3.1 --- Document Representation --- p.22 / Chapter 3.2 --- Classification Scheme Learning Strategy --- p.25 / Chapter 4 --- Linear Combination Approach --- p.30 / Chapter 4.1 --- Overview --- p.30 / Chapter 4.2 --- Linear Combination Approach - The Algorithm --- p.33 / Chapter 4.2.1 --- Equal Weighting Strategy --- p.34 / Chapter 4.2.2 --- Weighting Strategy Based On Utility Measure --- p.34 / Chapter 4.2.3 --- Weighting Strategy Based On Document Rank --- p.35 / Chapter 4.3 --- Comparisons of Linear Combination Approach and Existing Meta-Learning Methods --- p.36 / Chapter 4.3.1 --- LC versus Simple Majority Voting --- p.36 / Chapter 4.3.2 --- LC versus BORG --- p.38 / Chapter 4.3.3 --- LC versus Restricted Linear Combination Method --- p.38 / Chapter 5 --- The New Meta-Learning Model - MUDOF --- p.40 / Chapter 5.1 --- Overview --- p.41 / Chapter 5.2 --- Document Feature Characteristics --- p.42 / Chapter 5.3 --- Classification Errors --- p.44 / Chapter 5.4 --- Linear Regression Model --- p.45 / Chapter 5.5 --- The MUDOF Algorithm --- p.47 / Chapter 6 --- Incorporating MUDOF into Linear Combination approach --- p.52 / Chapter 6.1 --- Background --- p.52 / Chapter 6.2 --- Overview of MUDOF2 --- p.54 / Chapter 6.3 --- Major Components of the MUDOF2 --- p.57 / Chapter 6.4 --- The MUDOF2 Algorithm --- p.59 / Chapter 7 --- Experimental Setup --- p.66 / Chapter 7.1 --- Document Collection --- p.66 / Chapter 7.2 --- Evaluation Metric --- p.68 / Chapter 7.3 --- Component Classification Algorithms --- p.71 / Chapter 7.4 --- Categorical Document Feature Characteristics for MUDOF and MUDOF2 --- p.72 / Chapter 8 --- Experimental Results and Analysis --- p.74 / Chapter 8.1 --- Performance of Linear Combination Approach --- p.74 / Chapter 8.2 --- Performance of the MUDOF Approach --- p.78 / Chapter 8.3 --- Performance of MUDOF2 Approach --- p.87 / Chapter 9 --- Conclusions and Future Work --- p.96 / Chapter 9.1 --- Conclusions --- p.96 / Chapter 9.2 --- Future Work --- p.98 / Chapter A --- Details of Experimental Results for Reuters-21578 corpus --- p.99 / Chapter B --- Details of Experimental Results for OHSUMED corpus --- p.114 / Bibliography --- p.125

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