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Salient Object Detection Method Based on Refinement Spatial Consistency Two-Stage Graph

A consistent, two-stage technology, applied in the field of image processing, it can solve the problems of complex background, inability to detect salient objects completely and consistently, and inability to detect salient objects, etc., to achieve the effect of refining spatial consistency

Active Publication Date: 2021-05-04
XIDIAN UNIV
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Problems solved by technology

However, this method only considers the consistency of the neighborhood space, that is, the current node is connected to its adjacent nodes, and essentially only performs two simple calculations on a graph
Since only the spatial consistency of the neighborhood is considered, there may be "holes" inside the salient objects in the obtained saliency map, and the salient objects cannot be detected completely and consistently.
In addition, the consistency of neighborhood space can generally describe the relationship between nodes in simple scenes, but in complex scenes, such as the foreground and background are very similar or the background is very complex, it is difficult to describe the relationship between nodes well , so it is often impossible to detect salient targets

Method used

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Embodiment Construction

[0043] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0044] refer to figure 1 , the present invention comprises the following steps:

[0045] Step 1) Over-segment the input image:

[0046] The simple linear iterative clustering algorithm SLIC is used to over-segment the input image to obtain N superpixels. After comparing the experimental results of several commonly used settings N=50, 100, 150, 200, and 250, the number of segmentations with the best detection effect N=200 is obtained, and extracted The nine-dimensional color features of each superpixel: RGB, HSV and CIELab, constitute the feature matrix X of the input image, X=[x 1 ,x 2 ,...x i ..., x N ]∈R m×N , where m is the feature dimension, m=9, x i Denotes the feature vector of the i-th superpixel, conventionally x i The calculation method directly averages all pixel features in the i-th superpixel. This method is less r...

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Abstract

The invention discloses a salient target detection method based on a refined spatial consistency two-stage graph, which mainly solves the problem that the prior art cannot detect salient targets completely and consistently in complex scenes. The implementation plan is as follows: 1. Over-segment the input image to obtain several superpixels; 2. Construct the first-stage graph; 3. Construct a weighted joint robust sparse representation model; 4. Find the optimal weighted robust sparse representation model Optimal solution; 5. Calculate the saliency value of each node in the first stage graph; 6. Refine the spatial consistency and construct the second stage graph; 7. Use the popular ranking model to calculate the saliency value of each node in the second stage graph ; 8. Calculate the saliency value of each superpixel of the input image; 9. Output the pixel-level saliency map of the input image. The invention has good effects of foreground segmentation and background suppression, can completely and consistently detect image salient objects in complex scenes, and can be used for image preprocessing in computer vision.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a salient target detection method, in particular to a salient target detection method based on a refined spatial consistency two-stage graph, which can be used in the image preprocessing process in computer vision. [0002] technical background [0003] Salient object detection aims to detect objects in the scene that attract the attention of the human eye and are significantly different from the surrounding area, and segment the object from the scene. As an important image preprocessing method, salient object detection has been widely used in image processing fields such as image segmentation, image restoration, and object recognition. [0004] Existing object detection methods can be divided into two categories: one is supervised, task-specific top-down salient object detection methods, and the other is unsupervised, task-independent bottom-up While the salient object de...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/32G06K9/34
CPCG06V10/255G06V10/267
Inventor 张强刘毅姚琳韩军功王龙
Owner XIDIAN UNIV
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