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Selective Visual Attention: Computational Models and Applications by Weisi Lin, Liming Zhang

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8.4 Visual Attention for Image Retargeting

One popular application of visual attention models is image retargeting. The traditional image resizing method is to scale images by down-sampling uniformly. The problem with image scaling is that it will result in worse viewing experience and loss of some detailed information as the salient objects become smaller. Image cropping is an alternative solution which preserves the ROI in images by discarding other non-interest regions. The defect of this technique is that the context information in images will be lost [51, 52]. To overcome the limitations of image scaling and cropping, many effective image retargeting algorithms [53–63] have been proposed. In these algorithms, the content awareness is taken into consideration and a visual significance map is designed for measuring the visual importance of each pixel for the image resizing operation. The visual significance maps used in these algorithms are generally composed of a gradient map, a saliency map and some high-level feature maps such as facial map, motion map and so on [53–63]. In existing image retargeting algorithms, the saliency map can be used to measure the visual importance of image pixels for image resizing operations. This section will introduce a saliency-based image retargeting algorithm in the compressed domain [60]. This image retargeting algorithm adopts the saliency map in the compressed domain to measure the visual importance of image pixels for image resizing [60]. ...

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