A Saliency Detection Method Based on Level Set Superpixels and Bayesian Framework

A Bayesian framework and detection method technology, applied in the field of image processing, can solve problems such as too large image segmentation, affecting accuracy, and too small segmentation
CN106682679BActive Publication Date: 2019-08-09DALIAN UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Patents(China)
Current Assignee / Owner
DALIAN UNIV OF TECH
Publication Date
2019-08-09

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Abstract

The invention belongs to the field of image processing, relates to a saliency detection method based on level set superpixels and a Bayesian framework, and solves the problem of image saliency detection. First, the results of the level set method are segmented and merged to obtain new superpixels adapted to the size of different regions of the image. Second, a saliency map is constructed using the color and distance differences between image interior and edge superpixels. Then, new superpixels are used to represent salient regions, and three update algorithms are proposed under the Bayesian framework to update the saliency map to obtain saliency results, and the update algorithms can optimize the results of existing algorithms to a similar level. Finally, a detection algorithm based on face recognition is used to process pictures containing people. The method is able to identify the most salient parts in the image, while improving the results of existing algorithms to a more optimal level.
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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. ...

Claims

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