Mining Wikipedia Articles by Subject Headings and Video Summaries


Mining Wikipedia Articles by Subject Headings and Video Summaries – A new approach to automatically predicting the topics of articles on Wikipedia has been proposed by our co-investigators. We show that the prediction of the articles by topic alone produces promising results for a variety of applications beyond English Wikipedia. The goal is to predict the topic of articles on Wikipedia in a manner comparable to the ones produced by a prior knowledge base or the work of specialists in the field, including a large collection of existing research papers that cover a large range of topics. We propose here a new knowledge base that consists of two parts, a Knowledge Base Graph (KB) and a Learning Model. The KB helps to determine the topic of articles at a high level by predicting the number of citations to each article in the paper, which is then inferred to be an article’s topic. The KB predicts the topic of individual articles by identifying topic keywords for each article as it has been identified by a previous article. Experiments performed on the MNIST, AIM-SARIA and OMBR datasets demonstrate that the proposed method provides a promising performance.

We present an automated tracking system that can be made automatically accessible in the wild, without a manual intervention. The system is constructed from raw data of objects, and the user has to search the database to get images of objects to be tracked for a given set of objects in a collection. We focus on the object object category and the object category description of the object in the collection, and present an algorithm for generating categories. We show that as the number of object categories grown exponentially, more categories will be generated from these categories, and this is the case across all objects in the collection. We show that the system has three main steps: (1) the user searches the database for the object category in the collection, (2) the database image is generated and then has a search procedure to search by image for the category in the database, and (3) the object category is used to track when the system searches for the category for the objects in the collection. The system can capture the object descriptions accurately, and the system produces images of objects that are not easily identifiable by human eyes.

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Mining Wikipedia Articles by Subject Headings and Video Summaries

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    A Robust Batch Fisheye Transform for Multi-Object TrackingWe present an automated tracking system that can be made automatically accessible in the wild, without a manual intervention. The system is constructed from raw data of objects, and the user has to search the database to get images of objects to be tracked for a given set of objects in a collection. We focus on the object object category and the object category description of the object in the collection, and present an algorithm for generating categories. We show that as the number of object categories grown exponentially, more categories will be generated from these categories, and this is the case across all objects in the collection. We show that the system has three main steps: (1) the user searches the database for the object category in the collection, (2) the database image is generated and then has a search procedure to search by image for the category in the database, and (3) the object category is used to track when the system searches for the category for the objects in the collection. The system can capture the object descriptions accurately, and the system produces images of objects that are not easily identifiable by human eyes.


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