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Obviousness detection method based on grabcut and adaptive cluster clustering

A technology of adaptive clustering and detection method, applied in the field of image processing, can solve the problems of not being able to reflect the regionality and low calculation accuracy, and achieve the effect of reducing the amount of calculation without losing precision and high accuracy

Active Publication Date: 2017-02-22
DALIAN UNIV OF TECH
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AI Technical Summary

Problems solved by technology

A large number of superpixels can improve the calculation accuracy, but it cannot reflect the regionality of different parts of the image well; a small number of superpixels can well reflect the regionality of different parts of the image, but the calculation accuracy is low

Method used

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  • Obviousness detection method based on grabcut and adaptive cluster clustering
  • Obviousness detection method based on grabcut and adaptive cluster clustering
  • Obviousness detection method based on grabcut and adaptive cluster clustering

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Embodiment 1

[0053] figure 1 It is a schematic flow sheet of the inventive method; Fig. 2 (a), Fig. 2 (b), Fig. 2 (c), Fig. 2 (d), Fig. 2 (e), Fig. 2 (f) and Fig. 2 (g) respectively is the input image to be detected, the background part obtained by segmenting the whole image by the iterative graph cut algorithm, the initial saliency map, the region where the salient part is located, the result of iterative graph cut segmentation of the salient region, the final saliency map and the true value. Concrete steps for realizing the present invention for accompanying drawing are:

[0054] The first step is to use the simple linear iterative clustering algorithm SLIC to segment the image to obtain SLIC large superpixels and SLIC small superpixels respectively; divide the input image into 50 superpixels to obtain SLIC large superpixels, which are used to reflect different parts of the image The regionality; the input image is divided into 350 superpixels to obtain SLIC small superpixels, which are...

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Abstract

The invention belongs to the field of image processing and relates to an obviousness detection method based on grabcut and adaptive cluster clustering. The detection method can solve the problem of image obviousness detection and comprises the steps: firstly, using a simple linear iteration clustering (SLIC) algorithm to cut an image to obtain superpixels with different sizes; then, providing an initial obviousness image of an image constructed by an adaptive clustering algorithm based on the background portion of the image obtained through the grabcut algorithm; finally, using the grabcut algorithm to update an obvious area to obtain a final obviousness image according to the obvious portion of the initial obviousness image and by searching for the obvious area through the regions of the superpixels. The method can reduce the calculated amount without losing accuracy and has higher accuracy.

Description

technical field [0001] The invention belongs to the field of image processing and relates to a saliency detection method based on iterative graph cut and self-adaptive clustering. Background technique [0002] The purpose of image saliency detection is to find the most salient parts in the image, which reflect which areas in the image can attract people's attention and the degree of attention. Saliency detection algorithms can be divided into two categories: top-down methods and bottom-up methods. Top-down is usually for a specific task or target, using a supervised way to learn various features of the target, and then detecting the input picture, using the learned feature information to complete the salient target recognition, the shortcomings of this method It can only complete specific goals and must pass training, and the scalability is poor. The bottom-up method does not need to learn, and calculates directly through information such as pixels. The commonly used metho...

Claims

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Application Information

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IPC IPC(8): G06T7/11G06T7/187G06T7/90G06K9/62
CPCG06F18/232
Inventor 陈炳才周超姚念民高振国王健余超卢志茂谭国真
Owner DALIAN UNIV OF TECH
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