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基于兴趣点颜色及纹理特征的图像检索算法，通过同类比较，平均检索准确率提高11个百分点。-Based on the interest points and texture features of color image retrieval algorithm, through a similar comparison, the average retrieval accuracy rate of 11 percent improve.
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研究论文基于颜色和纹理特征图像检索技术的研究-Research papers based on color and texture features of image retrieval techniques
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提出了一种基于小波和Hough 变换的仿射不变性商标检索方法. 根据商标图像色彩调、纹理简单的特点, 首先对商标图像进行二值化处理并做Hough 变换 -Proposed a Hough transform based on wavelet and affine invariance trademark retrieval method according to trademark the image color tone, texture characteristics of a simple
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基于颜色矩和纹理谱的图像检索系统，可是实现准确率高的检索-Spectrum based on color moments and texture image retrieval system, but to achieve high accuracy of the retrieval
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基于颜色与纹理特征的图像检索技术，中北大学硕士毕业设计论文-Image retrieval based on color and texture features, in North University master' s graduate design thesis
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计算灰度共生矩阵的Matlab程序,参考《基于颜色空间和和纹理特征的图像检索》,输入图像数据,返回八维纹理特征行向量
-Computing to GLCM the Matlab program, the reference input color space and texture feature-based image retrieval, image data, return to the eight-dimensional texture feature row vector
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用Java写的图像检索代码，基于颜色，纹理和形状等特征-Image Retrieval code written in Java, based on characteristics such as color, texture and shape
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Image Retrieval Based On Color and Texture Features
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基于颜色特征和纹理特征的图像检索，请多多指教！-Image retrieval based on color and texture features, a great weekend!
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基于内容的图像检索（Content-based Image Retrieval，简称CBIR）技术被提出。这一技术的出现提高了图像检索的准确性，它通过提取图像本身的内在客观特征如颜色、纹理、形状、布局等关系，并比较这些视觉特征间的相似性，自动搜索出符合用户要求的图像。-Content-based image retrieval (Content-based Image Retrieval, referred to as CBIR) techniques have been proposed. T
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基于图像内容的检索（颜色和纹理特征）。由于里面用到的库可能和你配置不一样，需要自己配置下。-Based image retrieval (color and texture). Which used the database may be configured and you do not need their own configuration.
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This paper has a further exploration and study of visual feature extraction. According to the HSV (Hue, Saturation, Value) color space, the work of color feature extraction is finished, the process is as follows: quantifying the color space in non-eq
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基于颜色和纹理的综合图像检索方法论文，图像部分采用颜色直方图方法，纹理部分采用灰度共生矩阵特征向量方法-Integrated based on color and texture image retrieval papers, part of the image using the color histogram method, part of the use GLCM texture feature vector approach
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% 图像检索——纹理特征
%基于共生矩阵纹理特征提取，d=1,θ=0°,45°,90°,135°共四个矩阵
%所用图像灰度级均为256
%参考《基于颜色空间和纹理特征的图像检索》(% image retrieval - texture features
% based on co-occurrence matrix texture feature extraction, d=1, theta, =0 degrees, 45 degrees, 90 degrees, 135 degrees
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基于内容的图像检索，能搜索相似图像。基于颜色，形状和纹理的图像检索。(Content based image retrieval can search similar images. Image retrieval based on color, shape and texture.)
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