Personalized multi-modality image management and search for mobile devices
Mobile devices are quickly becoming a primary medium for personal information gathering, management, and sharing. Managing personal image data on mobile platforms is a challenging problem due to large data set size, content and context diversity, heterogeneous individual usage patterns, and resource constraints. This article presents a user-centric system, called iScope, for personal image management and sharing on mobile devices. iScope uses multi-modality clustering of both content and context information for efficient image management and search, and online learning techniques for predicting images of interest. It also supports distributed image search among networked devices while maintaining the same intuitive interface, enabling efficient information sharing among people. We have implemented iScope and conducted infield experiments using networked Nokia N810 portable Internet tablets. Energy efficiency was a primary design focus during the design and implementation of the iScope search algorithms. Experimental results demonstrate that iScope improves search time and search energy by 4.1and 3.8on average, relative to browsing.
management energy retrieval models performance