Detection method for salient regions of images based on geodesic line distance

Abstract

The invention provides a detection method for salient regions of images based on geodesic line distance. The method comprises the steps of S1, over-dividing input images into super-pixels of small areas, and calculating average colors and positions in the super-pixels; S2, trasversing all the super-pixels to obtain rough saliency energy values; S3, detecting and sequencing Harris angular points in the images, reserving a certain number of angular points with high scores, and searching minimum circumscribed convex hulls of the angular points; S4, setting the rough saliency energy values of the super-pixels outside the convex hulls to be 0, and keeping the saliency energy values of the super-pixels in the convex hulls to be unchanged; and S5, calculating final saliency values of the super-pixels in the convex hulls according to spreading saliency energy of geodesic line distance. According to the detection method for salient regions of images based on geodesic line distance, saliency objects or areas in the images can be uniformly highlighted; and simultaneously, background interference can be strongly restrained, and subsequent processing such as target object dividing is facilitated.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a geodesic distance-based image salient region detection method. Background technique [0002] Saliency detection (computation) is a current research topic in the field of computer vision, and its output is generally called a "saliency map". The brighter the area on the map, the easier it is to attract people's attention. Saliency detection has a wide range of applications in automatic object localization and segmentation, image retrieval, and content-based image scaling. Current saliency detection methods can generally be divided into two categories: bottom-up and top-down. The bottom-up saliency detection method simulates the human instinctive visual attention mechanism, and uses the underlying features such as color, edge, texture, etc. for detection. Top-down saliency detection methods utilize human visual attention mechanisms to be influenced by prior knowledge an...

AI Technical Summary

Problems solved by technology

Because the former introduces the conditional random field framework to fuse multiple features and segment salient objects, it often takes a long time to process a picture, so it is difficult to meet some real-time applications.

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