A Deep Learning Saliency Detection Method Based on Global Prior and Local Context

Abstract

The invention discloses a deep learning saliency detection method based on global prior and local context. First, superpixel segmentation is performed on color images and depth images. Based on the middle-level features such as compactness, uniqueness and background of each superpixel, Obtain the global prior feature map of each superpixel, and further pass the deep learning model to obtain the global prior saliency map; then, combine the global prior saliency map and the local context information in the color image and the depth image, and pass the deep learning model , to obtain the initial saliency map; finally, optimize the initial saliency map according to the spatial consistency and appearance similarity, and obtain the final saliency map. The application of the present invention solves the problem that the traditional saliency detection method cannot effectively detect salient objects in complex background images, and also solves the problem that the existing saliency detection method based on deep learning causes false detection due to noise in the extracted high-level features. question.

Description

technical field [0001] The invention belongs to the field of image processing and computer vision, in particular to a deep learning saliency detection method based on global prior and local context. Background technique [0002] When human eyes perceive the external environment, they can always extract interesting content from scenes containing a lot of information. This ability is called visual attention. Visual attention is a research hotspot in computer vision. There are two main aspects of research: one is to study eye gaze based on visual attention mechanism, and the other is to study the extraction of salient target regions, that is, saliency detection. The purpose of saliency detection is to separate the more eye-catching target area from the background in the image, and then extract the target and the information it carries, which is widely used in image segmentation, image recognition, video anomaly detection and other fields. [0003] At present, the research on s...

AI Technical Summary

Problems solved by technology

[0006] The problem to be solved by the present invention is: in the salient object detection technology of images, the salient objects in complex background images cannot be effectively detected simply by relying on manually set features and some prior knowledge; The saliency detection method based on deep learning only takes the original image or the local area of ​​the original image as the input of the deep learning model, which may cause false detection due to the noise of the extracted high-level features

Images