基于彩色基元特征的图像检索
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摘要
颜色、纹理和形状是描述图像特征的基本属性。在基于内容的图像检索中,图像特征提取是关键技术之一。本文对一些经典的特征提取算法进行了较为详细的研究,然后从基元(Textons)角度来分析图像内容,提出了四个新型的描述子来进行图像特征提取,它们分别是基元共生矩阵、彩色基元描述子、基元自相关图和多基元直方图。
灰度共生矩阵是一种经典的纹理分析方法。它能够表达相邻像素之间的空间相关性,但无法体现颜色和形状特征,因此本文提出了基元共生矩阵来描述图像特征。它首先将彩色图像量化为256种颜色并且直接在RGB颜色空间上提取图像边缘,然后定义5种特殊基元类型来进行基元分析,最后利用共生矩阵来表达基元图像特征,并应用于基于内容的图像检索。实验结果表明:基元共生矩阵能够描述图像颜色、纹理和形状特征,性能优于灰度共生矩阵和颜色相关图方法。
在均匀颜色空间中,任意两个颜色点之间的欧几里德距离对应于人类视觉系统存在的感知差异。根据均匀颜色空间的属性,本文提出了彩色基元描述子来描述图像特征。它首先将彩色图像从RGB颜色空间转化为Lab均匀颜色空间,并且将彩色图像均匀量化为64种颜色,然后定义了四种特殊基元类型来进行基元分析,最后统计基元图像的颜色差别来表达图像特征。实验表明:彩色基元描述子能够结合颜色、纹理和形状特征,检索性能优于边缘直方图描述子和LBP方法。
颜色自相关图能够结合颜色和空间信息,但它的计算量大,特征维数高。本文在统计相关图的基础上,提出了基元自相关图来描述彩色图像特征,并应用于基于内容的图像检索。它首先把彩色图像量化为64种颜色,然后定义4种特殊基元类型来进行基元分析,最后通过统计相关图来表达图像特征。实验结果表明:基元自相关图的检索性能优于颜色直方图和颜色自相关图。基元自相关图同样能够结合颜色和空间信息,特征维数和计算量均明显地小于颜色自相关图。
多基元直方图是对基元共生矩阵的一种重大改进。它通过构造4种特殊基元类型来进行多角度基元分析,然后利用共生矩阵来提取边缘方向和颜色之间的空间相关性,最后利用直方图来描述共生矩阵所表达的空间属性,并应用于基于内容的图像检索。多基元直方图不仅能够在颜色信息中体现边缘特征,而且还能够在边缘信息中体现颜色特征。实验结果表明:多基元直方图的检索性能优于边缘直方图描述子和边缘方向自相关图方法,能够有效地结合颜色、纹理和形状特征。
Color, Texture and Shape feathers are the basic attribute of image. In content -based image retrieval, feature extracting is of great importance. This paper analysis some classic algorithms about feature extracting, and put forward four new discriptors for features extracting and use to image retrieval. They are texton co-occurrence matrix, color textons discriptors, textons correlograms and mutil textons histogram, respectively.
Gray level co-occurrence matrices are a classic texture analysis method. It can express the spatial correlation of neighboring pixels, but it can not express color and shape features. This paper put forward texton co-occurrence matrix to describe image features. We have quantized the original images into 256 colors and computed color gradient from RGB color space, and then defined five special textons types for image analysis, and using co-occurrence matrix to describe image features. Image retrieval experimental results have shown that texton co-occurrence matrix method has the discrimination power of color, texture and shape features, the performances are better than that of gray level co-occurrence matrix and color correlograms method.
In perceptually uniform color space, we can measure the perceptual difference between two colors using the Euclidean distance. Thus, the perceptual difference between two colors can be measured in a very similar way to the human perception of colors. This paper put forward color textons descriptors to describe image features based on the attribute of perceptually uniform color space. We have quantized the original images into 64 colors and transform the original images from RGB color space into Lab color space, then defined four special types of textons for image analysis and then calculated the color differences of textons to describe image features. Image retrieval experimental results have shown that color textons descriptors has the discrimination power of color, texture and shape features, the performances are better than that of edge histogram descriptor and LBP method.
Color correlograms can combine color and spatial information, but its complexity and features dimension are big, thus this paper put forword textons correlograms to express image features. We have quantized the original images into 64 colors, and then defined four special types of textons for image analysis. Finally we have described the attribute of textons image by correlograms. Image retrieval experimental results have shown that the performances of textons correlograms are better than that of color histogram and color autocorrelograms. Textons correlograms also can combine color and spatial information, its complexity and features dimension are small than that of color autocorrelograms significantly.
Multi textons histogram is the improvement vision of texton co-occurence matrix. This method has effective utilized the information of edge orientation and color. We have defined four special types of textons for multi textons analysis. It can express the spatial correlation of edge orientation and color in textons images and use to image retrieval. Multi textons histogram can express not only the attribute of edge contain color imformation, but also the attribute of color contain edge imformation. The Experimental results have shown that Multi textons histogram has the discrimination power of color, texture and shape features, the performances are better than that of edge orientation auto-correlogram and edge histogram descriptor significantly.
灰度共生矩阵是一种经典的纹理分析方法。它能够表达相邻像素之间的空间相关性,但无法体现颜色和形状特征,因此本文提出了基元共生矩阵来描述图像特征。它首先将彩色图像量化为256种颜色并且直接在RGB颜色空间上提取图像边缘,然后定义5种特殊基元类型来进行基元分析,最后利用共生矩阵来表达基元图像特征,并应用于基于内容的图像检索。实验结果表明:基元共生矩阵能够描述图像颜色、纹理和形状特征,性能优于灰度共生矩阵和颜色相关图方法。
在均匀颜色空间中,任意两个颜色点之间的欧几里德距离对应于人类视觉系统存在的感知差异。根据均匀颜色空间的属性,本文提出了彩色基元描述子来描述图像特征。它首先将彩色图像从RGB颜色空间转化为Lab均匀颜色空间,并且将彩色图像均匀量化为64种颜色,然后定义了四种特殊基元类型来进行基元分析,最后统计基元图像的颜色差别来表达图像特征。实验表明:彩色基元描述子能够结合颜色、纹理和形状特征,检索性能优于边缘直方图描述子和LBP方法。
颜色自相关图能够结合颜色和空间信息,但它的计算量大,特征维数高。本文在统计相关图的基础上,提出了基元自相关图来描述彩色图像特征,并应用于基于内容的图像检索。它首先把彩色图像量化为64种颜色,然后定义4种特殊基元类型来进行基元分析,最后通过统计相关图来表达图像特征。实验结果表明:基元自相关图的检索性能优于颜色直方图和颜色自相关图。基元自相关图同样能够结合颜色和空间信息,特征维数和计算量均明显地小于颜色自相关图。
多基元直方图是对基元共生矩阵的一种重大改进。它通过构造4种特殊基元类型来进行多角度基元分析,然后利用共生矩阵来提取边缘方向和颜色之间的空间相关性,最后利用直方图来描述共生矩阵所表达的空间属性,并应用于基于内容的图像检索。多基元直方图不仅能够在颜色信息中体现边缘特征,而且还能够在边缘信息中体现颜色特征。实验结果表明:多基元直方图的检索性能优于边缘直方图描述子和边缘方向自相关图方法,能够有效地结合颜色、纹理和形状特征。
Color, Texture and Shape feathers are the basic attribute of image. In content -based image retrieval, feature extracting is of great importance. This paper analysis some classic algorithms about feature extracting, and put forward four new discriptors for features extracting and use to image retrieval. They are texton co-occurrence matrix, color textons discriptors, textons correlograms and mutil textons histogram, respectively.
Gray level co-occurrence matrices are a classic texture analysis method. It can express the spatial correlation of neighboring pixels, but it can not express color and shape features. This paper put forward texton co-occurrence matrix to describe image features. We have quantized the original images into 256 colors and computed color gradient from RGB color space, and then defined five special textons types for image analysis, and using co-occurrence matrix to describe image features. Image retrieval experimental results have shown that texton co-occurrence matrix method has the discrimination power of color, texture and shape features, the performances are better than that of gray level co-occurrence matrix and color correlograms method.
In perceptually uniform color space, we can measure the perceptual difference between two colors using the Euclidean distance. Thus, the perceptual difference between two colors can be measured in a very similar way to the human perception of colors. This paper put forward color textons descriptors to describe image features based on the attribute of perceptually uniform color space. We have quantized the original images into 64 colors and transform the original images from RGB color space into Lab color space, then defined four special types of textons for image analysis and then calculated the color differences of textons to describe image features. Image retrieval experimental results have shown that color textons descriptors has the discrimination power of color, texture and shape features, the performances are better than that of edge histogram descriptor and LBP method.
Color correlograms can combine color and spatial information, but its complexity and features dimension are big, thus this paper put forword textons correlograms to express image features. We have quantized the original images into 64 colors, and then defined four special types of textons for image analysis. Finally we have described the attribute of textons image by correlograms. Image retrieval experimental results have shown that the performances of textons correlograms are better than that of color histogram and color autocorrelograms. Textons correlograms also can combine color and spatial information, its complexity and features dimension are small than that of color autocorrelograms significantly.
Multi textons histogram is the improvement vision of texton co-occurence matrix. This method has effective utilized the information of edge orientation and color. We have defined four special types of textons for multi textons analysis. It can express the spatial correlation of edge orientation and color in textons images and use to image retrieval. Multi textons histogram can express not only the attribute of edge contain color imformation, but also the attribute of color contain edge imformation. The Experimental results have shown that Multi textons histogram has the discrimination power of color, texture and shape features, the performances are better than that of edge orientation auto-correlogram and edge histogram descriptor significantly.
引文
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