• Journal of KIISE:Software and Applications
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• v.29 no.7
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• pp.476-486
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• 2002

CBD(Component Based Development) has become the best strategical method for the business application. Because CBD is a new development paradigm which makes it possible to assemble the software components for application, it copes with the rapid challenge of business process and meets the increasing requirements for productivity. Since the business process is rapidly changing, CBD technology is the promising way to solve the productivity. Especially, the repository is the most important part for the development, distribution and reuse of components. In component repository, we can store and manage the related work-products produced at each step of component development as well as component itself. In this paper, we suggested a practical approach for repository construction to support and realize the CBD process and developed the CRMS(Component Repository Management System) as implementation product of the proposed techniques. CRMS can manage a variety of component products based on component architecture, and help software developers to search a candidate component for their project and to understand a variety of information for the component. In the paper, a practical approach for component repository was suggested, and a supporting environment was constructed to make CBD to be working efficiently. We expect this work wall be valuable research for component repository and the entire supporting Component Based Development Process.

• Journal of KIISE:Databases
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• v.33 no.4
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• pp.372-383
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• 2006

Among the approaches to image database management, content-based image retrieval (CBIR) is viewed as having the best support for effective searching and browsing of large digital image libraries. Typical CBIR systems allow a user to provide a query image, from which low-level features are extracted and used to find 'similar' images in a database. However, there exists the semantic gap between human visual perception and low-level representations. An effective methodology for overcoming this semantic gap involves relevance feedback to perform feature re-weighting. Current approaches to feature re-weighting require the number of components for a feature representation to be the same for every image in consideration. Following this assumption, they map each component to an axis in the n-dimensional space, which we call the metric space; likewise the feature representation is stored in a fixed-length vector. However, with the emergence of features that do not have a fixed number of components in their representation, existing feature re-weighting approaches are invalidated. In this paper we propose a feature re-weighting technique that supports features regardless of whether or not they can be mapped into a metric space. Our approach analyses the feature distances calculated between the query image and the images in the database. Two-sided confidence intervals are used with the distances to obtain the information for feature re-weighting. There is no restriction on how the distances are calculated for each feature. This provides freedom for how feature representations are structured, i.e. there is no requirement for features to be represented in fixed-length vectors or metric space. Our experimental results show the effectiveness of our approach and in a comparison with other work, we can see how it outperforms previous work.

• Journal of Broadcast Engineering
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• v.19 no.5
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• pp.578-589
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• 2014

Window image is displayed through a monitor screen when we execute the application programs on the computer. This includes webpage, video player and a number of applications. The webpage delivers a variety of information by various types in comparison with other application. Unlike a natural image captured from a camera, the window image like a webpage includes diverse components such as text, logo, icon, subimage and so on. Each component delivers various types of information to users. However, the components with different characteristic need to be divided locally, because text and image are served by various type. In this paper, we divide window images into many sub blocks, and classify each divided region into background, text and subimage. The detected subimages can be applied into 2D-to-3D conversion, image retrieval, image browsing and so forth. There are many subimage classification methods. In this paper, we utilize AdaBoost for verifying that the machine learning-based algorithm can be efficient for subimage detection. In the experiment, we showed that the subimage detection ratio is 93.4 % and false alarm is 13 %.