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12 May 2010 Online recursive estimation of attention and salient regions in visual scenes
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This paper describes an algorithm and system for rapidly generating a saliency map and finding interesting regions and in large-sized (i.e., extremely high-resolution) imagery and video. Previous methods of finding salient or interesting regions have a fundamental shortcoming: they need to process the entire image before the saliency map can be outputted and are therefore very slow for large images. Any prior attempts at parallelizing this operation involve computing feature maps on separate processors, but these methods cannot provide a result until the entire image has been processed. Rather than employing a single-step process, our system uses a recursive approach to estimate the saliency, processing parts of the image in sequence and providing an approximate saliency map for these regions immediately. With each new part of the image, a series of normalization factors is updated that connects all image parts analyzed so far. As more of the image parts are analyzed, the saliency map of the previously analyzed parts as well as newly analyzed parts becomes more exact. In the end, an exact global saliency map of the entire image is available. This algorithm can be viewed as (1) a fast, parallelizable version of prior art, and/or (2) a new paradigm for computing saliency in large imagery/video. This is critical, as the analysis of large, high-resolution imagery becomes more commonplace. This system can be employed in a default, bottom-up manner or a directed, top-down manner which will assign a preference to certain features over others. One can apply this system to any static scene, whether that is a still photograph or an image captured from video.
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Deepak Khosla and David J. Huber "Online recursive estimation of attention and salient regions in visual scenes", Proc. SPIE 7696, Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI, 769614 (12 May 2010);

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