基于纹理特征的一种图像检索方法的实现

提出了一种基于纹理特征的“区域－图像”的检索方法，该方法包括两种基本的技术：一是利用共生矩阵提取六种不同的纹理特征；二是利用归一化相关的方法进行纹理特征向量的相似性度量。通过实验，证明了该方法可以产生比较满意的检索结果。     

图像检索中LBP纹理谱旋转不变性研究

图像检索是要找到图像库中的相似图像。LBP纹理谱对旋转比较敏感,针对这一问题,将一个LBP纹理谱基元旋转一定的角度后得到的新纹理谱当成具有视觉一致性的纹理谱基元,将具有视觉一致性的所有纹理谱基元合并成一个,这个可以降低特征值的维度,而且可以解决图像旋转影响相似度计算的问题。用此纹理谱进行纹理图像描述,用纹理基元直方图进行检索,结果表明查准率比用规范化的LBP纹理谱进行检索有明显提高,检索效果较好。     

基于纹理和形状特征相结合的图像检索技术

图像检索是指从大型数据库提取所需的图像,可能是基于文本或基于内容的检索。在这里,基于内容的图像检索（CBIR）是一个长期的研究课题。Gabor滤波器用于提取图像的纹理特征,形态闭运算结合Gabor滤波器提供了更好的检索精度。通过应用Gabor滤波器计算出纹理特征的平均值和标准偏差,形成特征向量;使用傅立叶描述符和质心距离提取形状特征,利用纹理和形状特征相结合的方法来提高检索性能。     

基于手绘草图的自然纹理图像感知分析与检索

基于手绘草图的图像检索是图像检索的一个分支。由于手绘草图存在内容不详细,信息量少,变形严重等缺点,导致手绘草图和自然图像之间存在巨大差异,很难实现匹配,因此找到可靠的特征来同时表征自然图像和手绘草图非常重要。本文提出了一种基于手绘草图的自然纹理图像检索框架,将手绘草图的特征与自然纹理图像的特征相结合以获得具有人类感知约束的新特征向量,使用组合后的特征向量训练随机森林模型,用于预测相似性,并根据相似性进行排序,得到检索结果。实验结果表明该方法可以有效地实现基于手绘草图的自然纹理图像感知分析与检索。     

基于区域的图像检索技术

传统的基于内容的图像检索方法提取图像全局特征,缺乏高层语义信息,无法很好地满足用户的检索要求。本文深入研究基于区域的检索方法,该方法能够更好地表达用户的查询要求,在语义层次上理解图像、检索图像,是基于内容的图像检索的一种行之有效的方法。     

基于多区域中心加权卷积特征的图像检索

针对图像特征局部信息描述不足问题,提出一种基于多区域中心加权深度卷积特征提取方法。首先通过卷积神经网络提取输入图像的卷积层激活特征图,然后通过计算不同通道特征图的差异,选择具有区分性的区域特征图,最后通过多区域权重进行加权聚合,生成用于检索图像特征向量。在不同的建筑物数据集进行实验,结果表明检索精度分别提升了1.2%、0.9%。     

基于投影和纹理信息融合的医学图像检索

为克服基于单一特征进行医学图像检索存在的局限性,文章提出了一种新的基于投影和纹理信息融合的检索算法,分析医学图像的特点,引入颜色投影并选择综合性能较高的特征进行融合,调整各特征的权重,最后运用加权欧氏距离进行相似性度量．实验结果表明,该算法检索速度快,具有较高的查准率．     

融合颜色与LBP纹理特征的布料色卡图像检索

针对在采购与销售布料中存在人工配色不准确、查找分类难等问题,基于图像检索相关技术,通过融合布料色卡图像的颜色特征和纹理特征进行图像检索应用研究。设计了布料色卡图像颜色特征提取算法和基于LBP的纹理特征提取算法,并提出融合颜色特征和纹理特征的布料色卡检索算法。对提出的融合颜色和纹理特征的布料色卡图像检索算法在3个标准图像纹理库和实际布料色卡图像数据集进行了详细的对比测试。通过对测试结果的分析,得出在融合颜色和LBP纹理特征的布料色卡图像检索方法中采用先颜色后纹理的策略进行布料色卡图像检索是最有效的检索方案。     

基于颜色和纹理的图像检索

提出一种基于颜色和纹理特征的图像检索方法,采用颜色直方图表示图像的颜色特征,利用Gabor小波提取图像的纹理特征,为进一步提高图像检索的质量引入相关反馈机制,提出了一种动态调整两幅图像相似度中颜色特征和纹理特征的权值系数的方法。实验结果表明,该方法具有较好的检索性能。     

基于内容的图像档案检索

基于内容的图像检索技术是指利用图像内容对图像进行检索的技术.图像的内容包括图像的颜色特征、形状特征、纹理特征等.本文针对图像档案检索的问题,介绍了基于内容图像检索技术的概念,并给出了基于内容的图像档案检索系统的实现方案.     

A flower image retrieval method based on ROI feature

Flower image retrieval is a very important step for computer-aided plant species recognition.In this paper,we propose an efficient segmentation method based on color clustering and domain knowledge to extract flower regions from flower images.For flower retrieval,we use the color histogram of a flower region to characterize the color features of flower and two shape-based features sets,Centroid-Contour Distance(CCD)and Angle Code Histogram(ACH),to characterize the shape features of a flower contour.Experimental results showed that our flower region extraction method based on color clustering and domain knowledge can produce accurate flower regions.Flower retrieval results on a database of 885 flower images collected from 14 plant species showed that our Region-of-Interest(ROD based retrieval approach using both color and shape features can perform better than a method based on the global color histogram proposed by Swain and Ballard(1991)and a method based on domain knowledge-driven segmentation and color names proposed by Das et al.(1999).     

A flower image retrieval method based on ROI feature

INTRODUCTION There are about 250000 named species offlowering plants and many plant species that havenot been classified and named. Plant classificationand identification is a very old field. So far, thistime-consuming process has mainly been carriedout by taxonomists and/or botanists. A significantimprovement can be expected if the plant identifi-cation can be carried out by a computer automati-cally or semi-automatically with the aid of imageprocessing and computer vision techniqu…     

基于分层聚类相关反馈算法的图像检索技术研究

基于内容的图像检索研究的目的是实现自动地、智能地检索图像,研究的对象是使查询者可以方便、快速、准确地从图像数据库中查找特定图像的方法和技术。通过把分层聚类策略与传统的相关反馈算法相结合,提出一种新的图像检索方式,并通过实验加以验证。     

An image retrieval system based on fractal dimension

This paper presents a new kind of image retrieval system which obtains the feature vectors of images by estimating their fractal dimension; and at the same time establishes a tree-structure image database. After preprocessing and feature extracting, a given image is matched with the standard images in the image database using a hierarchical method of image indexing.     

An image retrieval system based on fractal dimension

INTRODUCTIONWiththerapiddevelopmentofthetechnologyofimagecollection ,storage,processionanddis play,digitalimagesarewidelyused (Zhangetal.,2 0 0 1 ) .However,itisdifficulttomanageahugeimagedatabase ,sothestudyoftheimagedatabasemanagementsystemandtheimagein…     

基于奇异值测度和颜色纹理的图像块特征向量

In this work, image feature vectors are formed for blocks containing sufficient information, which are selected using a singular-value criterion. When the ratio between the first two SVs are below a given threshold, the block is considered informative. A total of 12 features including statistics of brightness, color components and texture measures are used to form intermediate vectors. Principal component analysis is then performed to reduce the dimension to 6 to give the final feature vectors. Relevance of the constructed feature vectors is demonstrated by experiments in which k-means clustering is used to group the vectors hence the blocks. Blocks falling into the same group show similar visual appearances.     

基于形状特征的图像检索方法的研究与实现

在研究与分析了形状特征提取算法和相似性度量算法的基础上,采用欧式距离法,利用UIUC图库中的鸟类、蝴蝶类和Caltech101图像库中chair类、strawberrv类等4类图库,对Hu不变矩特征提取的图像检索算法进行验证,并对不同测试图库所得到的实验结果进行比较.从实验结果可以看出,对背景相对简单、对象的形态各异的图像还是具有较好的检索性能,可以得到较好的检索效果.     

图像检索中基于最大信息获取量的主动学习算法

本文提出一种基于内容的图像中的主动学习算法. 首先用支撑向量机学习得到初始查询概念, 然后用相似性测度对其进行检验, 选取信息量最大的样本来请求用户标记, 最后在相关反馈的迭代优化过程中获取用户的图像查询概念. 算法通过支撑向量机二值分类器与相似性测度2种不同学习模型的融合, 来减轻它们各自所存在的模型偏置. 实验结果显示, 所提算法能够显著提高图像检索的精确度, 在少量的反馈迭代之后即能准确地获取目标概念.     

Multi-core based parallel computing technique for content-based image retrieval

In this paper, we propose a parallel computing technique for content-based image retrieval （CBIR） system. This technique is mainly used for single node with multi-core processor, which is different from those based on cluster or network computing architecture. Due to its specific applications （such as medical image processing） and the harsh terms of hardware resource requirement, the CBIR system has been prevented from being widely used. With the increasing volume of the image database, the widespread use of multi-core processors, and the requirement of the retrieval accuracy and speed, we need to achieve a retrieval strategy which is based on multi-core processor to make the retrieval faster and more convenient than before. Experimental results demonstrate that this parallel architecture can significantly improve the performance of retrieval system. In addition, we also propose an efficient parallel technique with the combinations of the cluster and the multi-core techniques, which is supposed to gear to the new trend of the cloud computing.     

基于图像分解的分层图像修复

We propose a layered image inpainting scheme based on image decomposition.The damaged image is first decomposed into three layers:cartoon,edge,and texture.The cartoon and edge layers are repaired using an adaptive offset operator that can fill-in damaged image blocks while preserving sharpness of edges.The missing information in the texture layer is generated with a texture synthesis method.By using discrete cosine transform(DCT)in image decomposition and trading between resolution and computation complexity in texture synthesis,the processing time is kept at a reasonable level.