Finding important areas in images using conditional random field
نویسنده
چکیده
Finding importance areas from images has been an important topic in graphics, multimedia and vision. In this paper, we present a supervised learning approach. We first collect a training set of color images and the labeled importance maps. Then we apply supervised learning to predict the importance maps as a function of the image. Our model uses a grid-shaped conditional random field that incorporates multi-scale image features, and models both the importance label at each patch as well as the relation between importance labels at different patches.
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