Description: 基于VC++实现的基于灰度共生矩阵的图像检索：用灰度共生矩阵提取图像的纹理特征进行检索-Based on VC++ Realize the gray-level co-occurrence matrix-based image retrieval: The gray-scale extraction of co-occurrence matrix texture features of image retrieval Platform: |
Size: 216064 |
Author:王伟 |
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Description: 使用Weka分析环境开发的基于Java的图像分割及基于内容检索程序，分割采用最小生成树分割法，检索利用的是小波和共生矩阵提取的特征。-Analysis using the Weka environment for the development of Java-based content-based image segmentation and retrieval procedures, division of segmentation using minimum spanning tree, retrieval using wavelet and the extraction of the characteristics of co-occurrence matrix. Platform: |
Size: 1734656 |
Author:张沛轩 |
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Description: Hu的七个不变矩在图像匹配检索中适用广泛。本代码仅计算图像间Hu的七个矩的D2大小，直观的了解Hu矩的应用-seven moment invariants in image retrieval in the application of broad match. Only the calculation of the code between the image of the seven Hu moments D2 size, intuitive understanding of the application of Hu Moment Platform: |
Size: 356352 |
Author:oyq |
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Description: 实现了图象检索的功能,其中包括颜色直方图和灰度共生距阵.-The realization of the image retrieval function, including color histogram and the Gray Level Co-occurrence matrix. Platform: |
Size: 1415168 |
Author:tianyun |
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Description: 多媒体技术的发展和视觉信息的飞速膨胀迫切需要对视觉信息资源的有效管理和检索。由此，基于内容的图像和视频检索技术得到了越来越多的重视，成为了多媒体信息检索和图像处理领域中的重要研究方向。CBIR技术将对大规模图像信息的管理和访问提供有力支持。
本文这种介绍了内容图像检索的灰度特征实现方法，具有理论意义和实际应用价值。针对基于内容图像检索技术进行了研究，介绍了其研究现状和关键技术，讨论了其技术瓶颈和发展趋势。共生矩阵法，是对图像的所有像元进行统计调查，以便描述其灰度分布的一种方法。分析了这种基于广义图像灰度共生矩阵的图像检索方法，该方法首先将原图像作平滑处理得到平滑图像，然后将原图像和平滑图像组合得到广义图像灰度共生矩阵，提取该矩阵的统计特征量，最后将该统计量组成归一化向量用以检索。
实验结果表明，本方法的效果要优于单纯的灰度共生矩阵法。在对相似图像检索时，该算法与基于共生矩阵的经典算法相当，在面临同一图像大小显著改变或发生旋转的情况下，该算法明显优于经典算法。相比较而言，该算法提高了检索的有效性。
-The development of multimedia technology and the expansion of visual information，which cries for the effective management and retrieval methods of visual information. Thus, the image and video searching technology based on content is gaining more and more attention to become a multimedia information retrieval and image processing in the field of important research direction. The technology of CBIR provides effective supports for the management and access of extensive image information.
This paper introduced the content of the image retrieval method characteristics of gray, with theoretical and practical value. This paper does an extensive and in-depth study of technology based on content image retrieval to, presenting their research status and key technologies, discussing its development trends and technological bottlenecks. Co-occurrence matrix of all the pixel image survey has been conducted in order to describe a method of distribution of gray. This paper analyses this broad-based Platform: |
Size: 570368 |
Author:qichao |
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Description: 详细描述了基于颜色特征的图像检索技术 利用共生矩阵和综合模糊直方图检索-A detailed description of Color-based Image Retrieval technology, the use of co-occurrence matrix and synthesis of fuzzy histogram Search Platform: |
Size: 566272 |
Author:李雅琳 |
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Description: 文是一种基于灰度共生矩阵的图像相似性检索算法，首先将图像分成互不重叠的子图像块，根据子图像
块中像素间灰度差别重新设置每个像素的灰度值为0或l，然后将整个图像重新划分成子块，对子块编码，最后借
助共生矩阵提取的不同方向的特征值来检索图像的相似性。实验结果表明本文算法对图像相似性的检索比传统
方法GLCM、CCM更有效，且检索效率较高，更重要的是此算法还可以反应不同方向上图像的相似度。-Man is an image based on gray level co-occurrence matrix of similarity search algorithm, the first image into non-overlapping sub-image block, the sub-image blocks of pixels gray Cibie reset each pixel grayscale value 0 or l, then the image re-divided into sub-blocks, pair block coding, and finally extracted with co-occurrence matrix eigenvalues in different directions to retrieve similar images. Experimental results show that the algorithm for image similarity retrieval method than the traditional GLCM, CCM is more effective and efficient retrieval, more importantly, the algorithm can also respond in different directions on the image similarity. Platform: |
Size: 416768 |
Author:周二牛 |
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Description: 基于HSV颜色直方图和共生矩阵的邮票图像检索-Based on HSV color histogram and the co-occurrence matrix of the stamp image retrieval Platform: |
Size: 4096 |
Author:王璐 |
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Description: 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-equal intervals, constructing one dimension feature vector and representing the color feature by cumulative histogram. Similarly, the work of texture feature extraction isobtained by using gray-level co-occurrence matrix (GLCM) orcolor co-occurrence matrix (CCM). Through the
quantification of HSV color space, we combine color features and GLCM as well as CCM separately. Depending on the former, image retrieval based on multi-feature fusion is achieved by using normalized Euclidean distance classifier. Through the image retrieval experiment, indicate that the use of color features and texture based on CCM has obvious advantage.-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-equal intervals, constructing one dimension feature vector and representing the color feature by cumulative histogram. Similarly, the work of texture feature extraction isobtained by using gray-level co-occurrence matrix (GLCM) orcolor co-occurrence matrix (CCM). Through the
quantification of HSV color space, we combine color features and GLCM as well as CCM separately. Depending on the former, image retrieval based on multi-feature fusion is achieved by using normalized Euclidean distance classifier. Through the image retrieval experiment, indicate that the use of color features and texture based on CCM has obvious advantage. Platform: |
Size: 4478976 |
Author:mrinalini |
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Description: Based on the analysis of methods of CBIR and
chest image characteristic, in this paper, color
correlogam, dominant color of partition, gray level
co-occurrence matrix, gray-gradient co-occurrence
matrix and shape invariant moments were extracted as
retrieval feature. After comparison of their retrievals,
feature fusion and relevance feedback is proposed.
Experiments proved that the combining color, texture
with shape feature gets effective retrieval and relevance
feedback further more improves retrie Platform: |
Size: 900096 |
Author:Salkoum |
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Description: 基于Matlab环境的利用灰度共生矩阵实现的图像检索算法-To realize the image retrieval algorithe based on gray-level co-occurrence matrix by using Matlab Platform: |
Size: 2048 |
Author:张海涛 |
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Description: % 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, a total of four matrices
The gray level of the image used is 256
% refer to image retrieval based on color space and texture features) Platform: |
Size: 1024 |
Author:火焰约定
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Search - Image Retrieval CO