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It has been more than 40 years since the first digital imaging sensor was invented. For the last four decades, image acquisition functionality has continuously been improved, in terms of quality, size and cost. Consequently the digital imaging techniques started to be utilized in the digital camera industry and various component applications as well. According to the trend of the current applications of digital imaging, images are being produced not only by the conventional mechanism, such as regular camera, but also by the applications with digital image acquisition functionality, who aim to better understand the circumstances using an analysis of images. As a consequence of this trend, the amount of images created by diverse image related devices is immeasurably huge. Moreover the utilization of such voluminous amount of images demands a management system that can support information management operations, such as search, contents duplication and modification detection. These operations can be efficiently and effectively processed when the image related operations are performed by the usage of distinctive values, not by the image processing operations which require much more processing time. In order to meet the satisfaction of the requirement, we have been studied approaches that can utilize the line segments and the luminance areas. However, those approaches are disadvantage in the case of that an image contains the relatively large regions with sparse & Taeg Keun Whangbo edges or stationary luminance. In order to resolve such circumstances, in this paper, we present a more improved approach, in the perspective of cost and requirement satisfaction. The proposed method mainly exploits luminance areas and pixel location, so that the produced image identifiers could satisfy one-to-one relationship between images and identifiers. With experimental evaluations, we demonstrate that our approach is most favorable considering cost-effectiveness and efficiency.

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Due to the nature of an open operating system, Android framework cannot avoid the fragmentation problem. To address this, cost of time and efforts to devise application tests in mobile environment is unavoidable. In this paper, we propose two methods that handle android fragmentation problem.