Baoping Zhang

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The effectiveness of content-based image retrieval (CBIR) systems can be improved by combining image features or by weighting image similarities, as computed from multiple feature vectors. However, feature combination do not make sense always and the combined similarity function can be more complex than weight-based functions to better satisfy the users'(More)
In this paper, we propose a novel framework using <i>Genetic Programming</i> to combine image database descriptors for content-based image retrieval (CBIR). Our framework is validated through several experiments involving two image databases and specific domains, where the images are retrieved based on the shape of their objects.
This paper discusses how citation-based information and structural content (e.g., title, abstract) can be combined to improve classification of text documents into predefined categories. We evaluate different measures of similarity derived from the citation structure and the structural content of the collection, and determine how they can be fused to(More)
This paper shows how citation-based information and structural content (e.g., title, abstract) can be combined to improve classification of text documents into predefined categories. We evaluate different measures of similarity -- five derived from the citation information of the collection, and three derived from the structural content -- and determine how(More)
One goal of the Computing and Information Technology Interactive Digital Educational Library (CITIDEL) is to maximize the number of computing related resources available to computer science scholars and practitioners through it. In this paper, we describe a set of experiments designed to help this goal by adding to CITIDEL a sub-collection of computing(More)
Automatic text classification using current approaches is known to perform poorly when documents are noisy or when limited amounts of textual content is available. Yet, many users need access to such documents, which are found in large numbers in digital libraries and in the WWW. If documents are not classified, they are difficult to find when browsing.(More)
This paper shows how different measures of similarity derived from the citation information and the structural content (e.g., title, abstract) of the collection can be fused to improve classification effectiveness. To discover the best fusion framework, we apply Genetic Programming (GP) techniques. Our experiments with the ACM Computing Classification(More)