Website summarization: a topic hierarchy based approach.

Liu Nan.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references (leaves 84-88).Abstracts in English and Chinese.Abstract --- p.1Acknowledgements --- p.3Contents --- p.4List of Figures --- p.6List of Tables --- p.7Chapter Chapter 1 --- Introduction --- p.8Chapter Chapter 2 --- Related Work --- p.12Chapter 2.1 --- Web Structure Mining --- p.12Chapter 2.1.1 --- HITS Algorithm --- p.13Chapter 2.1.2 --- PageRank Algorithm --- p.13Chapter 2.2 --- Website Mining --- p.14Chapter 2.2.1 --- Website Classification --- p.14Chapter 2.2.2 --- Web Unit Mining --- p.16Chapter 2.2.3 --- Logical Domain Extraction --- p.16Chapter 2.2.4 --- Web Thesaurus Construction --- p.17Chapter Chapter 3 --- Website Topic Hierarchy Generation --- p.19Chapter 3.1 --- Problem Definition --- p.19Chapter 3.2 --- Graph Based Algorithms --- p.21Chapter 3.2.1 --- Breadth First Search --- p.21Chapter 3.2.2 --- Shortest Path Search --- p.23Chapter 3.2.3 --- Minimum Directed Spanning Tree --- p.24Chapter 3.2.4 --- Discussion --- p.27Chapter 3.3 --- Edge Weight Function --- p.28Chapter 3.3.1 --- Relevance Method --- p.29Chapter 3.3.2 --- Machine Learning Method --- p.32Chapter 3.4 --- Experiments --- p.47Chapter 3.4.1 --- Data Preparation --- p.47Chapter 3.4.2 --- Performances of Breadth-first Search --- p.50Chapter 3.4.3 --- Performances of Shortest-path Search --- p.50Chapter 3.4.4 --- Performances of Directed Minimum Spanning Tree --- p.54Chapter 3.4.5 --- Comparison of Different Algorithms --- p.55Chapter Chapter 4 --- Website Summarization Through Keyphrase Extraction --- p.58Chapter 4.1 --- Introduction --- p.58Chapter 4.2 --- Background --- p.60Chapter 4.3 --- Keyphrase Extraction --- p.69Chapter 4.3.1 --- Candidate Phrases Idenfication --- p.69Chapter 4.3.2 --- Feature Calculation without Topic Hierarchy --- p.70Chapter 4.3.3 --- Feature Calculation with Topic Hierarchy --- p.72Chapter 4.3.4 --- Extraction of Keyphrases --- p.75Chapter 4.4 --- Experiments --- p.76Chapter Chapter 5 --- Conclusion and Future Work --- p.82References: --- p.8

mARC: Memory by Association and Reinforcement of Contexts

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Multilayer Complex Network Descriptors for Color-Texture Characterization

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Predictive Modeling of Breast Cancer Diagnosis Using Neural Networks:A Kaggle Dataset Analysis

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A Literature Study On Video Retrieval Approaches

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Media aesthetics based multimedia storytelling.

Since the earliest of times, humans have been interested in recording their life experiences, for future reference and for storytelling purposes. This task of recording experiences --i.e., both image and video capture-- has never before in history been as easy as it is today. This is creating a digital information overload that is becoming a great concern for the people that are trying to preserve their life experiences. As high-resolution digital still and video cameras become increasingly pervasive, unprecedented amounts of multimedia, are being downloaded to personal hard drives, and also uploaded to online social networks on a daily basis. The work presented in this dissertation is a contribution in the area of multimedia organization, as well as automatic selection of media for storytelling purposes, which eases the human task of summarizing a collection of images or videos in order to be shared with other people. As opposed to some prior art in this area, we have taken an approach in which neither user generated tags nor comments --that describe the photographs, either in their local or on-line repositories-- are taken into account, and also no user interaction with the algorithms is expected. We take an image analysis approach where both the context images --e.g. images from online social networks to which the image stories are going to be uploaded--, and the collection images --i.e., the collection of images or videos that needs to be summarized into a story--, are analyzed using image processing algorithms. This allows us to extract relevant metadata that can be used in the summarization process. Multimedia-storytellers usually follow three main steps when preparing their stories: first they choose the main story characters, the main events to describe, and finally from these media sub-groups, they choose the media based on their relevance to the story as well as based on their aesthetic value. Therefore, one of the main contributions of our work has been the design of computational models --both regression based, as well as classification based-- that correlate well with human perception of the aesthetic value of images and videos. These computational aesthetics models have been integrated into automatic selection algorithms for multimedia storytelling, which are another important contribution of our work. A human centric approach has been used in all experiments where it was feasible, and also in order to assess the final summarization results, i.e., humans are always the final judges of our algorithms, either by inspecting the aesthetic quality of the media, or by inspecting the final story generated by our algorithms. We are aware that a perfect automatically generated story summary is very hard to obtain, given the many subjective factors that play a role in such a creative process; rather, the presented approach should be seen as a first step in the storytelling creative process which removes some of the ground work that would be tedious and time consuming for the user. Overall, the main contributions of this work can be capitalized in three: (1) new media aesthetics models for both images and videos that correlate with human perception, (2) new scalable multimedia collection structures that ease the process of media summarization, and finally, (3) new media selection algorithms that are optimized for multimedia storytelling purposes.Postprint (published version