A framework for attention-based personal photo manager

In this paper, we propose a novel framework for the design and implementation of an attention-based personal digital photo browsing platform. The key concept that separates the proposed system from existing ones is the incorporation of user interaction patterns to infer the level of interest in a particular photo. Specifically, we use Web cameras to record and analyze the viewing behavior of the user and attempt to correlate the interest of the viewer to the effective viewing time. We also devise an updating scheme to efficiently renew the timing parameter. To build a comprehensive photo browser, external EXIF data and face detection results are utilized to coarsely classify the digital images. Moreover, measures of image quality, including sharpness and contrast, are calculated to rank the search results. Finally, a ranking-based algorithm is utilized to integrate the clues acquired from different modules.