基于自适应缩放图像多尺度超图的显著性检测方法研究
发布时间：2018-10-31 16:03
【摘要】：显著性检测是图像处理和计算机视觉领域的一个重要研究内容。本文探讨利用独立通道自适应缩放图像的多尺度超图进行显著性检测的方法。所做工作对图像处理和计算机视觉领域的发展具有重要意义。目前经典的显著性检测方法有很多,但已有方法几乎都没有考虑图像的R,G,B通道值差异对目标显著性检测结果的影响。本文将图像的R,G,B通道值差异引入基于超图建模的显著性检测方法中,提出了一种利用独立通道自适应缩放进行显著性检测的方法。具体研究内容包括:(1)考虑到人眼对R,G,B三原色的敏感度差异,对图像像素值进行独立通道自适应缩放,得到初始图像的独立通道自适应缩放图像;(2)将像素值的独立通道自适应缩放方法引入超顶点和超边构造中,并将自适应缩放因子与固定尺度经验值相结合得到自适应的多尺度超边和超顶点,从而设计基于独立通道自适应缩放图像的自适应多尺度超图;(3)将所设计的基于独立通道自适应缩放图像的自适应多尺度超图引入显著性检测方法中,设计基于独立通道自适应缩放图像超图的显著性检测方法,并利用图像分割实例展示所设计的显著性检测方法的有效性。首先利用梯度图计算基于独立通道自适应缩放图像的超顶点和超边的显著度,得到多尺度超图的显著性图,然后对多尺度超图的显著性图进行融合,得到最终的基于独立通道自适应缩放图像超图的显著性图,最后将所设计的显著性检测方法应用于图像分割实例以说明其有效性;(4)将所设计的基于独立通道自适应缩放图像超图的显著性检测方法在公开可获得的图像集MSRA-1000、SOD、SED和ImgSal-5上进行了测试,并与先前的6种经典显著性检测方法进行了比较。大量实验表明,所提出的方法对于R,G,B通道值范围差异较窄的图像在一定程度上改善了显著性检测效果。本文提出的基于独立通道自适应缩放图像的显著性检测方法可应用于图像分割、目标识别、图像自适应压缩、基于内容的图像检索等许多图像处理和计算机视觉应用中,有助于改善其性能、提高其效率。
[Abstract]:Salience detection is an important research content in the field of image processing and computer vision. In this paper, we discuss a method to detect salience by using the multi-scale hypergraph of an adaptive scaling image with independent channels. The work done is of great significance to the development of image processing and computer vision. At present, there are many classical significance detection methods, but almost all of the existing methods do not consider the influence of the difference of RGG channel value on the target significance detection results. In this paper, the difference of RG B channel value of image is introduced into the significance detection method based on hypergraph modeling, and a new method of significance detection based on adaptive scaling of independent channel is proposed. The specific research contents are as follows: (1) considering the sensitivity difference of human eyes to RGZB, the pixel value of the image is scaled by independent channel adaptively, and the original image can be scaled by independent channel adaptively; (2) the independent channel adaptive scaling method of pixel value is introduced into hypervertex and hyper-edge construction, and the adaptive multi-scale super-edge and hypervertex are obtained by combining the adaptive scaling factor with the fixed scale empirical value. An adaptive multi-scale hypergraph based on independent channel adaptive scaling image is designed. (3) the adaptive multi-scale hypergraph based on the independent channel adaptive scaling image is introduced into the salience detection method, and the significance detection method based on the independent channel adaptive scaling image hypergraph is designed. An example of image segmentation is used to demonstrate the effectiveness of the proposed salience detection method. Firstly, using gradient graph to calculate the saliency of hypervertex and super-edge based on independent channel adaptive scaling image, the significance graph of multi-scale hypergraph is obtained, and then the significance graph of multi-scale hypergraph is fused. Finally, the final saliency graph based on independent channel adaptive scaling image hypergraph is obtained. Finally, the proposed saliency detection method is applied to the image segmentation example to demonstrate its effectiveness. (4) the salience detection method based on independent channel adaptive scaling image hypergraph is tested on the publicly available image sets MSRA-1000,SOD,SED and ImgSal-5. And compared with the previous six classical significance detection methods. A large number of experiments show that the proposed method can improve the significant detection effect to some extent for the images with narrow range of RGN B channel values. The salience detection method based on independent channel adaptive scaling image can be used in many image processing and computer vision applications, such as image segmentation, target recognition, image adaptive compression, content-based image retrieval and so on. It is helpful to improve its performance and improve its efficiency.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41
本文编号：2302799
[Abstract]:Salience detection is an important research content in the field of image processing and computer vision. In this paper, we discuss a method to detect salience by using the multi-scale hypergraph of an adaptive scaling image with independent channels. The work done is of great significance to the development of image processing and computer vision. At present, there are many classical significance detection methods, but almost all of the existing methods do not consider the influence of the difference of RGG channel value on the target significance detection results. In this paper, the difference of RG B channel value of image is introduced into the significance detection method based on hypergraph modeling, and a new method of significance detection based on adaptive scaling of independent channel is proposed. The specific research contents are as follows: (1) considering the sensitivity difference of human eyes to RGZB, the pixel value of the image is scaled by independent channel adaptively, and the original image can be scaled by independent channel adaptively; (2) the independent channel adaptive scaling method of pixel value is introduced into hypervertex and hyper-edge construction, and the adaptive multi-scale super-edge and hypervertex are obtained by combining the adaptive scaling factor with the fixed scale empirical value. An adaptive multi-scale hypergraph based on independent channel adaptive scaling image is designed. (3) the adaptive multi-scale hypergraph based on the independent channel adaptive scaling image is introduced into the salience detection method, and the significance detection method based on the independent channel adaptive scaling image hypergraph is designed. An example of image segmentation is used to demonstrate the effectiveness of the proposed salience detection method. Firstly, using gradient graph to calculate the saliency of hypervertex and super-edge based on independent channel adaptive scaling image, the significance graph of multi-scale hypergraph is obtained, and then the significance graph of multi-scale hypergraph is fused. Finally, the final saliency graph based on independent channel adaptive scaling image hypergraph is obtained. Finally, the proposed saliency detection method is applied to the image segmentation example to demonstrate its effectiveness. (4) the salience detection method based on independent channel adaptive scaling image hypergraph is tested on the publicly available image sets MSRA-1000,SOD,SED and ImgSal-5. And compared with the previous six classical significance detection methods. A large number of experiments show that the proposed method can improve the significant detection effect to some extent for the images with narrow range of RGN B channel values. The salience detection method based on independent channel adaptive scaling image can be used in many image processing and computer vision applications, such as image segmentation, target recognition, image adaptive compression, content-based image retrieval and so on. It is helpful to improve its performance and improve its efficiency.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41
【参考文献】
相关博士学位论文 前1条
1 景慧昀;视觉显著性检测关键技术研究[D];哈尔滨工业大学;2014年
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