基于边缘颜色分布的图像检索方法

提出了一种新的基于边缘颜色分布的图像检索算法。该算法将不同类型边缘附近的颜色分布作为刻画图像内容的主要特征,设计了一种紧凑的2D边缘颜色直方图来对图像的边缘颜色分布特征进行描述，既利用了局部颜色特征，又考虑了不同类型边缘的空间分布信息，克服了传统颜色直方图不能反映空间信息的缺陷。实验结果表明，该算法与其他同类方法相比，有效地提高了检索性能。     

基于分块颜色相关向量的图像检索算法

阐述了颜色相关向量的基本概念，提出了分块颜色相关向量相似性度量的计算方法和相关区域快速搜索算法，最终形成基于分块颜色相关向量的图像检索算法。实验表明，算法更符合人的主观感觉。     

基于九分块的颜色直方图图像检索算法

随着社会和科技的发展,人们对基于内容的图像检索的要求越来越高,但因研究起点较晚,技术还不是很成熟。文中就图像检索所用到颜色、纹理、形状、轮廓的基本颜色特征中比较重要的颜色特征为主要研究对象,从传统的基于全局颜色直方图及后来发展的一些改进的颜色直方图等思路出发,提出了一种基于九分块的颜色直方图方法,提高了图像检索的效率。     

基于全局特征的图像检索技术

在对海量的图像数据进行检索时,要得到好的检索效果,传统的信息检索方法已不能满足要求,这样,基于内容的图像检索(CBIR:Content-Based Image Retrieval)技术应运而生。本文讨论的是CBIR中的关键问题—特征描述,本文主要介绍全局特征描述,包括颜色、纹理和形状特征。     

基于颜色和边缘方向的图像检索方法

提出了一种新的图像特征表示方法,首先提取图像的底层颜色信息获取颜色特征值 ,通过对图像中物体的边缘检测计算像素点的边缘方向角度值,并对颜色特征值和边缘方向 角度值进行量化。然后根据相邻像素点之间量化结果的数值分析,为每个像素点建立8维特 征向量。再以中心像素点与相邻像素点间不同的位置关系为基础,为每种位置关系赋予不同 的权重,根据像素点的特征向量计算出图像中每一个像素点的特征值。最后统计图像中具有 相同特征值的像素点个数,形成特征直方图,以此作为图像检索的依据。实验表明本文方法 能够有效描述图像的颜色分布和图像中物体的空间结构,更加细致地记录图像信息,进一步 增强图像之间的区分能力。与其他方法相比,本文方法检索效果更好。     

基于颜色特征的图像检索技术研究

本文针对利用图像的颜色特征丢失空间信息的问题,提出了一种通过提取灰度特征,获取图像的形状特征,综合检索图像的方法。     

颜色直方图在基于内容的遥感影像检索中的比较分析

本文主要针对颜色直方图在基于内容的图像检索中的应用效果进行实验分析和性能比较。分别在RGB和HSV 颜色空间中共构造了6 类不同量化方式的直方图,并采用特征向量相似性度量和概率相似性度量方式对其进行了遥感影像的检索实验和查准率比较,实验结果表明记为Q4096 和Q256 的两种直方图在基于x2 统计距离下的检索查准率较高,期望为基于颜色特征进行图像内容检索的应用提供参考。     

基于全局不变矩和分块主颜色的图像检索算法

为充分发挥全局不变矩对图像形状信息着重考虑的优势,规避颜色直方图未曾考虑图像的空间位置以及常见分块策略忽略分块间的相互联系等弊端,提出一种改进的全局不变矩和分块主颜色相结合的图像检索算法。计算图像的全局不变矩,提取图像各分块主颜色,将两者进行归一化处理后进行线性加权求和,计算综合相似度。实验结果表明,该算法具有较高的检索精度,能够提高检索性能。     

基于内容图像检索中的颜色特征描述

多媒体数据库应用需要有效的基于内容相似性检索方法。颜色特征由于其简单、计算复杂度低及对几何变换的不变性成为机器可自动提取的图像内容中最重要的特征。文章讨论了颜色特征的表示及其进展。直方图是使用最普遍的颜色特征描述符,它必须选择与人类视觉机制一致的颜色空间和量化模式。直方图与空间关系的组合可提高图像内容描述的精度,因而提供更好的颜色特征匹配。由于基于小波变换的编码技术已成为JPEG2000等图像编码标准的核心,因此基于小波变换系数特征描述方法已越来越受到重视。由于图像颜色的心理作用可影响观察者对图像的理解,如何建立与心理活动及视觉机制相适应的颜色特征模型,提取这些语义级的高级抽象内容是我们必须面对的挑战。     

综合颜色和纹理特征的图像检索算法

基于内容的图像检索是当前多媒体信息检索的热点之一。基于内容的图像检索技术是根据对图像内容(特征)的描述和提取,在图像库中找到具有指定内容(特征)的图像。本文对图像颜色特征和纹理特征的提取、相似性度量等基于内容的图像检索的关键技术进行了分析和研究,并在此基础上,提出了一个基于颜色特征和纹理特征的图像检索算法并验证了其有效性。该算法采用HSV颜色空间的直方图作为颜色特征向量,采用灰度共生矩阵的四个纹理特征:能量、熵、惯性矩和相关性构成纹理特征向量,采用欧氏距离进行相似性度量。实验结果表明,该算法实现的系统具有良好的图像检索功能。     

基于颜色的图象检索技术

论述了基于色彩的图象检索方法,对颜色空间的选择、颜色的量化以及颜色的相似测试进行了讨论。在此基础上,对两种典型的基于颜色的图象检索方法的特点、关键技术的实现以及存在的根本问题进行了详细的分析和比较。     

基于SOM神经网络的颜色图像分割

1.引言基于内容的图像检索(Content-Based Image Retrieval)已广泛应用于生化、军事、文化、教育等领域。其原理是利用图像自身的特征,如颜色、纹理、形状及对象空间关系等信息,建立图像的特征矢量以进行检索。它与传统的基于文本的图像检索相比,能够更充分地利用图像的语义信息。在CBIR方法中,用户检索时通常可以提供样本图像,检索系统将该样本图像同图像库中的图像按一定方法进行比较,将与样本图像相似的图像返回给用户。     

Exploring statistical correlations for image retrieval

Bridging the cognitive gap in image retrieval has been an active research direction in recent years, of which a key challenge is to get enough training data to learn the mapping functions from low-level feature spaces to high-level semantics. In this paper, image regions are classified into two types: key regions representing the main semantic contents and environmental regions representing the contexts. We attempt to leverage the correlations between types of regions to improve the performance of image retrieval. A Context Expansion approach is explored to take advantages of such correlations by expanding the key regions of the queries using highly correlated environmental regions according to an image thesaurus. The thesaurus serves as both a mapping function between image low-level features and concepts and a store of the statistical correlations between different concepts. It is constructed through a data-driven approach which uses Web data (images, their surrounding textual annotations) as training data source to learn the region concepts and to explore the statistical correlations. Experimental results on a database of 10,000 general-purpose images show the effectiveness of our proposed approach in both improving search precision (i.e. filter irrelevant images) and recall (i.e. retrieval relevant images whose context may be varied). Several major factors which have impact on the performance of our approach are also studied.     

Ego-similarity measurement for relevance feedback

Relevance Feedback in Content-Based Image Retrieval is an active field of research. Many mechanisms of Relevance Feedback exist with many interactive techniques and implement criteria. In this paper, we proposed a novel approach of RF which can set adaptive weights of similarity measurement for each database image from the user feedback, i.e. ego-similarity measurement. We would explore the feedback records were archived in the two different ways that stored along with query images (QRF-based) or along with each retrieved relevant image from the image database (DBRF-based). In the experiment, DBRF-based relevant feedback improved greatly in the retrieval effectiveness.     

Content-based image retrieval using joint correlograms

The comparison of digital images to determine their degree of similarity is one of the fundamental problems of computer vision. Many techniques exist which accomplish this with a certain level of success, most of which involve either the analysis of pixel-level features or the segmentation of images into sub-objects that can be geometrically compared. In this paper we develop and evaluate a new variation of the pixel feature and analysis technique known as the color correlogram in the context of a content-based image retrieval system. Our approach is to extend the autocorrelogram by adding multiple image features in addition to color. We compare the performance of each index scheme with our method for image retrieval on a large database of images. The experiment shows that our proposed method gives a significant improvement over histogram or color correlogram indexing, and it is also memory-efficient.

农作物秸秆膨化机加工性能试验研究

利用小型自热式单螺杆挤压膨化机对农作物秸秆进行膨化加工试验。将螺杆螺距,喷嘴出口间隙,秸秆物料含水率,秸物料粒度作为试验因素,经单因素试验和二次通用旋转组合设计试验找出其对秸秆膨化加工性能（膨化压力,生产率,度电量等）的影响规律,并得出最佳参数组合。     

基于小波分析和Oracle8i的图像数据库系统设计

采用了小波分析以及小波零树编码技术来提取图像特征,在Oracle8i支持图像数据库查询的基础上,综合使用传统的数据库检索技术和初步走向应用的基于内容的查询技术,实验证明这种方法丰富了传统的关系型数据库的功能,并具有良好的检索性能。     

Image retrieval model based on weighted visual features determined by relevance feedback

An accurate and rapid method is required to retrieve the overwhelming majority of digital images. To date, image retrieval methods include content-based retrieval and keyword-based retrieval, the former utilizing visual features such as color and brightness, and the latter utilizing keywords that describe the image. However, the effectiveness of these methods in providing the exact images the user wants has been under scrutiny. Hence, many researchers have been working on relevance feedback, a process in which responses from the user are given as feedback during the retrieval session in order to define a user’s need and provide an improved result. Methods that employ relevance feedback, however, do have drawbacks because several pieces of feedback are necessary to produce an appropriate result, and the feedback information cannot be reused. In this paper, a novel retrieval model is proposed, which annotates an image with keywords and modifies the confidence level of the keywords in response to the user’s feedback. In the proposed model, not only the images that have been given feedback, but also other images with visual features similar to the features used to distinguish the positive images are subjected to confidence modification. This allows for modification of a large number of images with relatively little feedback, ultimately leading to faster and more accurate retrieval results. An experiment was performed to verify the effectiveness of the proposed model, and the result demonstrated a rapid increase in recall and precision using the same amount of feedback.