A Deep Learning Saliency Detection Method Based on Global Prior and Local Context
A deep learning and context technology, applied in the field of image processing and computer vision, can solve the problems of inability to effectively detect salient objects in complex background images, misdetection of high-level features, etc., and achieve reduced learning ambiguity, good robustness, and high detection results accurate effect
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[0033] The invention provides a deep learning saliency detection method based on global prior and local context. The method first performs superpixel segmentation on color images and depth images, and based on middle-level features such as compactness, uniqueness and background of superpixels , through the global prior deep learning model, the global prior saliency map is calculated; combined with the global prior saliency map and the local context information in the color image and the depth image, the initial saliency map is obtained through the deep learning model; finally, according to the spatial consistency The initial saliency map is optimized based on the similarity of sex and appearance, and the final saliency map is obtained. The invention is suitable for image salience detection, has good robustness, and the detection result is accurate.
[0034] Such as figure 1 Shown, the present invention comprises the following steps:
[0035] 1) Use the SLIC superpixel segmen...
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