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

Active Publication Date: 2019-08-09
DALIAN UNIV OF TECH
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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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  • A Saliency Detection Method Based on Level Set Superpixels and Bayesian Framework
  • A Saliency Detection Method Based on Level Set Superpixels and Bayesian Framework
  • A Saliency Detection Method Based on Level Set Superpixels and Bayesian Framework

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Embodiment Construction

[0043] Embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. This embodiment is implemented on the premise of the technical solution of the present invention, and detailed implementation and specific operation process are provided, but the protection scope of the present invention is not limited to the following embodiments.

[0044] The proposed algorithm is tested on four standard databases: Pascal-S database, which contains 850 pictures, some of which have complex backgrounds, and the complexity of the database is relatively high. ECSSD database, which contains 1000 pictures with different sizes and multiple targets. The MSRA database contains pixel-level real-value annotations, and the complexity of the picture is high. DUT-OMRON database, which contains 5168 pictures, contains pixel-level ground-truth annotations, the picture background is complex, and the target size is different, which is very challenging....

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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.

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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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06K9/46G06K9/00
CPCG06V40/168G06V10/462G06F18/23213
Inventor 陈炳才周超高振国余超姚念民卢志茂谭国真
Owner DALIAN UNIV OF TECH
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