Significance detection method based on level-set super pixel and Bayesian framework

Abstract

The invention belongs to the image processing field and relates to a significance detection method based on level-set super pixel and Bayesian frame to solve the detection problem of image significance. The method comprises the following steps: firstly, segmenting and combining the result of a level-set method to obtain new super pixels that meet the sizes of the different areas of the image; secondly, using the differences in colors and distances among the super pixels of the image inner part and the image edge part to construct a significance image; then, using the super pixels to represent the significance areas; putting forward three updated algorithms under the Bayesian framework; updating the significance image to obtain a significance result; increasing the currently available algorithm result to a similar level by the updated algorithms; and at least, using the detection algorithm based on human face recognition to process the image containing a person. The method of the invention is capable of recognizing the most significance part in an image and of increasing the result of a currently available algorithm to a higher level.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to a saliency detection method based on level set superpixels and a Bayesian frame. Background technique [0002] Image saliency detection is a challenging problem in computer vision. Image saliency is an important visual feature in an image, which reflects which areas in the image can attract people's attention and the degree of attention. Saliency detection algorithms can be divided into two categories: data-driven bottom-up methods and task-driven top-down methods. The top-down method is usually aimed at a specific target or task. It needs to use a supervised method to learn the color, shape and other characteristics of the target, and then use the learned information to detect the input picture and complete the specific recognition. , the disadvantage of this type of method is that it must be trained, and can only achieve specific goals, and the scalability of the method is poor. ...

AI Technical Summary

Problems solved by technology

However, the image segmentation results obtained by the level set method often have the problem that the image segmentation is too large or too small, resulting in unclear or too small segmentation of different regions, which will affect the accuracy.

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