A second generation contourlet transform based texture image retrieval system is proposed.
提出了一种基于第二代轮廓波变换的纹理检索系统的实现方法。
Referring to the low retrieval rate of basic contourlet transform, a Non-subsampled Contourlet Transform (NSCT) texture image retrieval system is proposed.
针对基本轮廓波变换纹理检索系统检索率较低的问题，提出了一种无下采样轮廓波变换（NSCT）纹理图像检索系统。
It presents the model and feature of content-based image retrieval system, and then discusses some methods of feature abstraction and similarity measurement based on color, texture and shape.
首先叙述了基于内容的图像检索的系统模型和特点，接着针对颜色、纹理和形状进行了概率特征提取、相似度量等的进一步具体分析讨论。
A new image retrieval method by using Max correlation min distance to combine together texture features, Gaussian density characteristics and face detection of images for image retrieval is presented.
提出利用最大相关最小距离将图像的纹理特征、高斯密度特征与人脸检测相结合的算法进行图像检索。
On the basis of which, an improved co-occurrence matrix and histogram are developed to extract the texture and shape features for the image retrieval.
在此基础上，采用一种改进的基于纹理基元的共生矩阵来获取纹理特征，并结合纹理基元的形状直方图来进行图像检索。
A method used for two-stage image retrieval with rotation-invariant texture feature is proposed against the common rotation problem of image.
针对图像中常见的旋转问题提出一种旋转不变纹理特征进行两级图像检索的方法。
A new method for pathology image retrieval by combining color, texture and morphologic features to search cell images is proposed.
提出了一种结合颜色、纹理和形状特征的细胞病理图像检索方法。
Traditional image retrieval methods is mainly dependent on the single vision features such as color texture shape and so on. So its retrieval result is not always ideal.
传统的图像检索主要依赖颜色、纹理、形状、空间关系等单一视觉特征，检索效果往往不够理想。
In this paper, out methods for image retrieval using center and texture features are firstly discussed. Furthermore, a new method for image retrieval using combined center and texture is proposed.
本文介绍了基于纹理特征的特征提取方法和中心特征的提取方法，并进而提出了一种综合利用上述两个特征共同进行检索的方法。
The statistical features of the texture images is computed directly from DCT coefficients, and used for image retrieval.
该方法在DCT压缩域，通过直接对DCT系数计算，获得图像纹理的统计特征，并作为检索的依据。
Traditionally image retrieval mainly relies on single feature such as color, texture, shape and spatial relationship, etc., so the result is usually not so good.
传统的基于内容的图像检索主要依赖颜色、纹理、形状、空间关系等单一视觉特征，检索效果往往不够理想。
Base on generalization and analysis of co-occurrence matrix algorithms, a method is proposed that could make fast acquisition of target image texture features and thus implement image retrieval.
在总结和分析共生矩阵算法的基础上，提出了一个快速获取目标图像纹理特征，进而实现图像检索的方法。
A novel image retrieval method based on color and texture feature is proposed.
提出了一种基于颜色和纹理特征的图像检索方法。
The main features used for image retrieval are color, texture and shape. This thesis looks into image retrieval techniques based on color and texture.
基于内容的图像检索技术主要利用图像的颜色、纹理和形状特征对图像进行检索，论文重点研究基于颜色和纹理特征的图像检索技术。
In this paper, we using a kind of comprehensive image retrieval which fuses color and texture features by linear weights and discuss the method which the weights are determined.
在检索中，颜色和纹理特征的权重不同，本文采用线性加权方式综合颜色特征相似距离和纹理特征相似距离，对图像进行检索。
Texture is an important item of image information, texture-based image retrieval has been an active research area, and the similarity comparison of texture features is a key to image retrieval.
纹理是图像的重要属性，基于纹理特征检索图像是当前的研究热点，对图像的纹理进行相似性比较是进行图像检索的关键。
The evaluation of texture similarity is very important in content-based image retrieval systems.
纹理相似性研究是基于内容检索研究中的一个重要组成部分。
In this paper, our methods for image retrieval using color and texture features are first discussed.
本文介绍了我们设计的分别基于颜色特征和基于纹理特征的两种图像检索算法。
This paper presents two feature fusion algorithms of medical image retrieval about endoscopic image based on FCM as follow:first, using color correlogram combining with color texture;
针对医学内窥镜图像，提出两种基于模糊C-均值聚类（FCM）的特征融合算法：融合颜色相关图和图像纹理特征算法以及融合颜色直方图和颜色相关图算法。
This paper presents two feature fusion algorithms of medical image retrieval about endoscopic image based on FCM as follow:first, using color correlogram combining with color texture;
针对医学内窥镜图像，提出两种基于模糊C-均值聚类（FCM）的特征融合算法：融合颜色相关图和图像纹理特征算法以及融合颜色直方图和颜色相关图算法。
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