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A Saliency Detection Method Combining Boundary Connectivity and Local Contrast

A detection method and connectivity technology, applied in the field of image processing, can solve problems such as unsatisfactory results

Active Publication Date: 2021-07-09
DALIAN UNIV OF TECH
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Problems solved by technology

In recent years, random walk models have also been used in image saliency detection. For example, Sun et al. used superpixels on the left and upper borders of images as absorbing nodes in the random walk model to calculate the initial saliency value of each superpixel. The practice is actually to use the boundary prior, but the effect is not satisfactory when the salient object appears at the edge of the image

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  • A Saliency Detection Method Combining Boundary Connectivity and Local Contrast
  • A Saliency Detection Method Combining Boundary Connectivity and Local Contrast
  • A Saliency Detection Method Combining Boundary Connectivity and Local Contrast

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Embodiment 1

[0067] The present invention tests the proposed algorithm on three standard databases: the ECSSD database, which contains 1000 pictures of different sizes and with multiple objects, some of which are taken from the very difficult Berkeley 300 database. The MSRA10K database, which is an extension of the MSRA database, contains 10,000 images, covering all 1,000 images in the ASD dataset, including many complex background images. 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. All three databases have corresponding manually calibrated saliency region maps.

[0068] figure 1 It is a schematic flow sheet of the method of the present invention; figure 2 It is a comparison chart of the saliency detection results of the present invention and other different algorithms. The concrete steps that realize the present invention are:

[0069] I...

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Abstract

The invention belongs to the field of image processing, relates to a saliency detection method combining boundary connectivity and local contrast, and solves the problem of image saliency detection. First, the SLIC algorithm is used to divide the superpixels, and the convex hull surrounding the foreground area is obtained by using the local contrast features through Harris corner detection. Then the clustering algorithm is used to remove the background area in the convex hull, and the obtained foreground area is used as the absorption node of the random walk model, and the intra-cluster propagation optimization is performed to obtain the foreground probability of each superpixel. At the same time, the background probability of each superpixel is calculated by using the characteristics of the boundary connectivity of each region. Finally, a saliency map is obtained by combining the foreground probability and background probability of each superpixel, and the final saliency map is obtained by suppressing the saliency values ​​of the background superpixels. This method can identify the most salient parts in the image and obtain a saliency map that is closer to the ground truth map.

Description

technical field [0001] The invention belongs to the field of image processing and relates to a saliency detection method combining boundary connectivity and local contrast. Background technique [0002] The purpose of image saliency detection is to find the most salient parts in the image. The salient parts indicate which areas in the image can attract people's attention and the degree of attention. Finding salient parts efficiently and quickly can greatly improve the efficiency of image processing. Saliency detection algorithms can be divided into two categories: top-down methods and bottom-up methods. Top-down is usually aimed at specific tasks, using a supervised way to learn various features of the target, and using the learned feature information to complete the recognition of salient targets in the image. The disadvantage of this type of method is that it can only complete specific targets. And must pass the training, the expansibility is poor. The bottom-up method c...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/194G06T7/143G06T7/187
CPCG06T2207/10004G06T7/143G06T7/187G06T7/194
Inventor 陈炳才陶鑫潘伟民余超年梅姚念民卢志茂
Owner DALIAN UNIV OF TECH
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