Traffic image segmentation method based on improved multi-object harmony search algorithm

A search algorithm and image segmentation technology, applied in the field of image processing, can solve the problems of low segmentation accuracy, small application area, and unsatisfactory segmentation results.

Inactive Publication Date: 2015-09-02
HUNAN UNIV +1
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AI Technical Summary

Problems solved by technology

At present, most image segmentation methods based on multi-objective optimal clustering only consider two segmentation criteria, and each type of image has its own characteristics, so there are shortcomings such as small application range, unsatisfactory segmentation results, and low segmentation accuracy.
[0005] In the existing traffic image segmentation methods, whether it is the multi-threshold segmentation

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  • Traffic image segmentation method based on improved multi-object harmony search algorithm

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Embodiment 1

[0066] The invention proposes a traffic image segmentation method based on an improved multi-object harmony search algorithm, which belongs to the technical field of image processing and mainly relates to the traffic image segmentation technology. The simulation in this example is carried out in the hardware environment of Intel(R) Core(TM) 2Duo CPU T5870 with a main frequency of 2.00GHZ, a memory of 1.96GB and a software environment of MATLAB R2010a.

[0067] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0068] Step 1, input a grayscale image I to be segmented, the grayscale image is saved with a nonlinear scale of 8 bits of sampling pixels, and has 256 levels of grayscale.

[0069] In this embodiment, input a traffic image I with roads and multiple vehicles 1 , see figure 2 (a), its size is 250×214, the grayscale histogram GH={h of statistical image I l ,l=0,1,...,255}, h l The iteration counter t is set for the number...

Embodiment 2

[0109] The traffic image segmentation method based on the improved multi-target harmony search algorithm is the same as embodiment 1, and in embodiment 2, the comparison experiment is K-means, fuzzy C-means image segmentation method, K-means, fuzzy C-means image segmentation method The number of segmentation categories K is set to 3. Input traffic image I with road, vehicle, obstacle in embodiment 2 2 , which has a size of 276×223 pixels and a grayscale of 256. Refer to 3(b) for the figure of the final classification result obtained by the present invention.

[0110] image 3 (a) is traffic image I 2 The original grayscale image of , image 3 (b), image 3 (c), image 3 (d) are respectively the segmentation result diagrams obtained by the method of the present invention, K-means, and fuzzy C-means methods. from image 3 (b) It can be seen that the present invention automatically converts image I 2 It is divided into three categories, vehicles and obstacles are one cat...

Embodiment 3

[0112] The traffic image segmentation method based on the improved multi-target harmony search algorithm is the same as embodiment 1, and in embodiment 3, the comparison experiment is K-means, fuzzy C-means image segmentation method, K-means, fuzzy C-means image segmentation method The number of segmentation categories K is set to 3. Input the traffic image 1 that has road in embodiment 3 3 , which has a size of 318×245 pixels and a grayscale of 256. Refer to 4(b) for the figure of the final classification result obtained by the present invention.

[0113] Figure 4 (a) is traffic image I 3 The original grayscale image of , Figure 4 (b), Figure 4 (c), Figure 4 (d) are respectively the segmentation result diagrams obtained by the method of the present invention, K-means, and fuzzy C-means methods. from Figure 4 (b) It can be seen that the present invention automatically converts image I 3 It is divided into three categories, roads are one category, road sidelines a...

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Abstract

The invention discloses a traffic image segmentation method based on an improved multi-object harmony search algorithm, and mainly solves problems of a conventional traffic image segmentation technology that the evaluation index is single, the anti-noise performance is poor, the segmentation accuracy is not high, and the number of image segmentation types is required by be determined manually. The implementation steps mainly comprise reading a gray scale image and counting a gray scale histogram; initializing a harmony memory bank; generating a group of new explanations, wherein the size the new explanations is equal to the size the harmony memory bank; updating the harmony memory bank; determining whether a termination condition is met or not; selecting the optimal cluster center from the harmony memory bank according to a PBM evaluation index; and classifying pixels of the image according to the optimal cluster center to obtain a segmentation result. Compared with the conventional traffic image segmentation technology, the method provided by the invention is diversified in evaluation index, high in segmentation accuracy and high in anti-noise performance, can automatically determine the number of segmentation types of the image, and can be applied to the traffic image segmentation.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a traffic image segmentation method based on an improved multi-object harmony search algorithm. Background technique [0002] The quality of image segmentation results directly affects the quality of subsequent image processing. Therefore, image segmentation is one of the key and difficult points in the field of image understanding. This difficult task has brought great challenges to researchers. The purpose of image segmentation is to divide an image into several non-overlapping areas with specific meanings, the same area has similar characteristics, and different areas have great differences. [0003] The existing image segmentation methods can be mainly divided into edge-based segmentation methods, region-based segmentation methods, and cluster-based segmentation methods. The essence of most clustering methods is the optimization problem of the objective function. D...

Claims

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T7/10G06T2207/30252G06T2207/30256G06T2207/30261
Inventor 袁小芳戴香山向永忠王耀南
Owner HUNAN UNIV
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