18 research outputs found

    Semantically enhanced document clustering

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    This thesis advocates the view that traditional document clustering could be significantly improved by representing documents at different levels of abstraction at which the similarity between documents is considered. The improvement is with regard to the alignment of the clustering solutions to human judgement. The proposed methodology employs semantics with which the conceptual similarity be-tween documents is measured. The goal is to design algorithms which implement the meth-odology, in order to solve the following research problems: (i) how to obtain multiple deter-ministic clustering solutions; (ii) how to produce coherent large-scale clustering solutions across domains, regardless of the number of clusters; (iii) how to obtain clustering solutions which align well with human judgement; and (iv) how to produce specific clustering solu-tions from the perspective of the user’s understanding for the domain of interest. The developed clustering methodology enhances separation between and improved coher-ence within clusters generated across several domains by using levels of abstraction. The methodology employs a semantically enhanced text stemmer, which is developed for the pur-pose of producing coherent clustering, and a concept index that provides generic document representation and reduced dimensionality of document representation. These characteristics of the methodology enable addressing the limitations of traditional text document clustering by employing computationally expensive similarity measures such as Earth Mover’s Distance (EMD), which theoretically aligns the clustering solutions closer to human judgement. A threshold for similarity between documents that employs many-to-many similarity matching is proposed and experimentally proven to benefit the traditional clustering algorithms in pro-ducing clustering solutions aligned closer to human judgement. 4 The experimental validation demonstrates the scalability of the semantically enhanced document clustering methodology and supports the contributions: (i) multiple deterministic clustering solutions and different viewpoints to a document collection are obtained; (ii) the use of concept indexing as a document representation technique in the domain of document clustering is beneficial for producing coherent clusters across domains; (ii) SETS algorithm provides an improved text normalisation by using external knowledge; (iv) a method for measuring similarity between documents on a large scale by using many-to-many matching; (v) a semantically enhanced methodology that employs levels of abstraction that correspond to a user’s background, understanding and motivation. The achieved results will benefit the research community working in the area of document management, information retrieval, data mining and knowledge management

    Analyse et recherche d'oeuvres d'art 2D selon le contenu pictural

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    État de l'art des méthodes manuelles et automatiques d'analyse des oeuvres d'art 2D -- Recherche d'images selon l'organisation spatiale des couleurs -- Seuil automatique pour la recherche d'images selon l'OSC -- Extraction des contours des traits -- Analyse de l'impact pictural dans les oeuvres au trait -- Conclusion et perspectives

    Learning task-specific similarity

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2006.Includes bibliographical references (p. 139-147).The right measure of similarity between examples is important in many areas of computer science. In particular it is a critical component in example-based learning methods. Similarity is commonly defined in terms of a conventional distance function, but such a definition does not necessarily capture the inherent meaning of similarity, which tends to depend on the underlying task. We develop an algorithmic approach to learning similarity from examples of what objects are deemed similar according to the task-specific notion of similarity at hand, as well as optional negative examples. Our learning algorithm constructs, in a greedy fashion, an encoding of the data. This encoding can be seen as an embedding into a space, where a weighted Hamming distance is correlated with the unknown similarity. This allows us to predict when two previously unseen examples are similar and, importantly, to efficiently search a very large database for examples similar to a query. This approach is tested on a set of standard machine learning benchmark problems. The model of similarity learned with our algorithm provides and improvement over standard example-based classification and regression. We also apply this framework to problems in computer vision: articulated pose estimation of humans from single images, articulated tracking in video, and matching image regions subject to generic visual similarity.by Gregory Shakhnarovich.Ph.D

    The Third NASA Goddard Conference on Mass Storage Systems and Technologies

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    This report contains copies of nearly all of the technical papers and viewgraphs presented at the Goddard Conference on Mass Storage Systems and Technologies held in October 1993. The conference served as an informational exchange forum for topics primarily relating to the ingestion and management of massive amounts of data and the attendant problems involved. Discussion topics include the necessary use of computers in the solution of today's infinitely complex problems, the need for greatly increased storage densities in both optical and magnetic recording media, currently popular storage media and magnetic media storage risk factors, data archiving standards including a talk on the current status of the IEEE Storage Systems Reference Model (RM). Additional topics addressed System performance, data storage system concepts, communications technologies, data distribution systems, data compression, and error detection and correction

    Automatic non-linear video editing for home video collections

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    The video editing process consists of deciding what elements to retain, delete, or combine from various video sources so that they come together in an organized, logical, and visually pleasing manner. Before the digital era, non-linear editing involved the arduous process of physically cutting and splicing video tapes, and was restricted to the movie industry and a few video enthusiasts. Today, when digital cameras and camcorders have made large personal video collections commonplace, non-linear video editing has gained renewed importance and relevance. Almost all available video editing systems today are dependent on considerable user interaction to produce coherent edited videos. In this work, we describe an automatic non-linear video editing system for generating coherent movies from a collection of unedited personal videos. Our thesis is that computing image-level visual similarity in an appropriate manner forms a good basis for automatic non-linear video editing. To our knowledge, this is a novel approach to solving this problem. The generation of output video from the system is guided by one or more input keyframes from the user, which guide the content of the output video. The output video is generated in a manner such that it is non-repetitive and follows the dynamics of the input videos. When no input keyframes are provided, our system generates "video textures" with the content of the output chosen at random. Our system demonstrates promising results on large video collections and is a first step towards increased automation in non-linear video editin

    Data Mining

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    The availability of big data due to computerization and automation has generated an urgent need for new techniques to analyze and convert big data into useful information and knowledge. Data mining is a promising and leading-edge technology for mining large volumes of data, looking for hidden information, and aiding knowledge discovery. It can be used for characterization, classification, discrimination, anomaly detection, association, clustering, trend or evolution prediction, and much more in fields such as science, medicine, economics, engineering, computers, and even business analytics. This book presents basic concepts, ideas, and research in data mining

    Connected Attribute Filtering Based on Contour Smoothness

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