基于颜色直方图的图像检索技术

使用颜色空间分布熵来表示图像的颜色空间分布特征,结合图像的颜色直方图特征,采用加权综合法和比例系数法表示图像的综合特征,设计了基于颜色直方图和图像空间分布熵的图像检索算法.利用查全率和查准率对算法进行了评价.通过实验分析比较可知,所设计的方法具有较好的查准率和查全率.     

基于分块颜色直方图的快速图像检索

针对传统颜色直方图不能反映图像空间颜色位置信息的问题,提出一种基于分块颜色直方图的检索方法,该方法先对一幅图像进行合理的分块,然后提取包含了图像位置信息的分块颜色特征,对提取的颜色特征采用相似度计算的方法,计算出两幅图像之间的相似度。实验结果表明,该方法不仅提高了图像检索的速度,同时也提高了检索的准确度。     

结合空间关系的颜色直方图检索算法

首先根据所选取的图像库的特征,对真彩图像各颜色分量赋以权重,利用直方图检索,得到检索结果与权重比例大小选择的关系;再针对直方图检索没有表达颜色的空间分布信息的缺点,在一般直方图检索的基础上引入了一维的空间关系矢量,将直方图和空间关系结合起来进行检索,提高了检索的性能。     

基于图像直方图混合度量匹配的图像检索

本文通过分析图像的空间信息和颜色之间的相关性，提出一种基于直方图混合度量的图像检索方法。实验证明本文提出的方法其检索效果明显优于传统直方图方法。     

基于颜色特征的少数民族服饰图像检索

针对同一民族的服饰具有统一的色调,文章使用RGB颜色空间下的颜色直方图来表示少数民族服饰图像的颜色特征,并在自建的少数民族服饰图像库中进行图像检索实验,在进行少数民族服饰图像检索时,使用改进的L1距离公式进行相似性度量,并与不同的相似性度量方法进行了对比研究,实验结果表明,与L1距离、L2距离、卡方距离和Canberra距离等相似性度量方法相比,文章提出的相似性度量方法检索效果较好。     

基于颜色特征的图像检索方法

为了提高图像数据库,特别是大型图像数据库查询的速度及准确度,文中提出了基于HSV颜色空间的两层数据库查询的方法.首先,利用简单有效的颜色矩对图像数据库筛选,缩小查询范围;然后,对图像进行4×4重叠分块,提取各分块的颜色直方图进行第二层图像数据库查询,以获取更准确的检索结果.这样分块的好处是既获得了图像的空间分布信息,又突出图像中间的主体部分和充分限制背景的范围,并且比起独立分块的方法减少了计算量.实验表明:根据用户给出的示例图像,通过两层查询技术能够获得较好的检索效果.     

十种基于颜色特征图像检索算法的比较和分析

在基于内容的图像检索中,颜色特征已得到广泛应用.本文对十种利用颜色特征进行图像检索的算法利用同一图像库进行了实验比较.实验结果表明,无论在HSI空间或MTM空间,累加直方图法均优于一般直方图法,对这一点本文还首次给出了严格的理论证明.实验结果还表明,加权距离法比欧氏距离法总体上没有明显改善,MTM空间比HSI空间也没有显出优势,而中心矩法算法简单,检索速度快,通过调整加权系数,检索精度可以接近累加直方图法.本文的实验和分析对选择和优化检索算法有一定的参考价值.     

基于颜色和空间特征的图像检索

在环形颜色空间划分和颜色视觉关注度的基础上,提出一种新的基于颜色和空间分布特征检索算法.首先,在HSV色彩空间下进行颜色量化并计算每种颜色的质心,进行环形颜色空间分割.然后计算各环形颜色空间直方图,在此基础上计算图像的空间颜色分布矩.同时计算各种颜色对应像素的平均视觉关注度,以此作为该颜色特征的视觉关注特征.最后对特征向量进行高斯归一化,采用特征向量的L1-norm距离计算彩色图像的相似度并进行图像检索.实验结果表明,本文算法比SCH及Geostat方法具有更好的检索效果.     

基于颜色空间分布熵的图像检索

颜色直方图是基于内容的图像检索(CBIR)中的一种重要特征,然而其完全丢失了图像颜色的空间分布信息.文中采用颜色空间熵描述图像颜色的空间分布特征,为了消除熵的对称性对图像检索的影响,提出了改进算法.通过实验比较表明了该方法在进行图像检索时是有效的,并具有较高的检索性能.     

基于颜色特征图像检索方法的研究

对颜色特征进行了分析,对颜色空间的选取,颜色的量化,颜色相似度进行了描述,论述了基于颜色特征图像检索的主要方法直方图相交法,主要颜色表示法,基于参考颜色表方法,基于区域的颜色法及其它们的改进方法,并作出了相应的比较.     

Robust image hash function using local color features

Conventional image hash functions only exploit luminance components of color images to generate robust hashes and then lead to limited discriminative capacities. In this paper, we propose a robust image hash function for color images, which takes all components of color images into account and achieves good discrimination. Firstly, the proposed hash function re-scales the input image to a fixed size. Secondly, it extracts local color features by converting the RGB color image into HSI and YCbCr color spaces and calculating the block mean and variance from each component of the HSI and YCbCr representations. Finally, it takes the Euclidian distances between the block features and a reference feature as hash values. Experiments are conducted to validate the efficiency of our hash function. Receiver operating characteristics (ROC) curve comparisons with two existing algorithms demonstrate that our hash function outperforms the assessed algorithms in classification performances between perceptual robustness and discriminative capability.     

A fast and efficient image retrieval system based on color and texture features

We propose a fast and efficient image retrieval system based on color and texture features. The color features are represented by color histograms and texture features are represented by block difference of inverse probabilities (BDIP) and block variation of local correlation coefficients (BVLC). It is observed that color features in combination with the texture features derived on the brightness component provides approximately similar results when color features are combined with the texture features using all three components of color, but with much less processing time. An analysis of various distance measures reveals that the square-chord distance measure outperforms the other prominent distance measures for the proposed method. Detailed experimental analysis is carried out using precision and recall on four datasets: Corel-5K, Corel-10K, UKbench and Holidays. The time analysis is also performed to compare processing speeds of the proposed method with the existing similar best methods.     

Dictionary-based color image retrieval using multiset theory

Dictionaries have recently attracted a great deal of interest as a new powerful representation scheme that can describe the visual content of an image. Most existing approaches nevertheless, neglect dictionary statistics. In this work, we explore the linguistic and statistical properties of dictionaries in an image retrieval task, representing the dictionary as a multiset. This is extracted by means of the LZW data compressor which encodes the visual patterns of an image. For this reason the image is first quantized and then transformed into a 1D string of characters. Based on the multiset notion we also introduce the Normalized Multiset Distance (NMD), as a new dictionary-based dissimilarity measure which enables the user to retrieve images with similar content to a given query. Experimental results demonstrate a significant improvement in retrieval performance compared to related dictionary-based techniques or to several other image indexing methods that utilize classical low-level image features.     

一种改进的综合BTC颜色矩和DT-CWT的图像检索算法

摘 要：特征提取是基于内容的图像检索中的关键技术。针对基于单一特征检索效果不理想的问题,提出一种改进的综合颜色和纹理特征的图像检索算法。该算法在YIQ颜色空间中进行特征提取,首先结合方块编码（BTC）的思想,提取颜色矩作为颜色特征;采用双树复小波变换（DT-CWT）提取纹理特征,融合两种特征并利用相似性度量方式进行图像检索。实验结果表明算法所提取的颜色、纹理特征更利于检索,使用综合特征检索的平均查准率比同类算法更高。     

低照度环境监控的图像增强算法研究

在档案库房安防系统中,光照是图像清晰度的重要因素,对后续处理和最终监管有着至关重要的影响.本文针对灰暗天气和夜间等低对比度图像,通过采用直方图均衡化(HE)、自适应直方图均衡化(AHE)和受限对比度自适应直方图均衡化算法(CLAHE)进行图像处理和对比,提出了一种基于受限对比度自适应直方图均衡化的改进算法.该方法首先进行了RGB和彩色空间转换,其次仅对亮度分量进行受限对比度自适应直方图均衡化变换和非线性拉伸变化,最后做RGB的图像输出.实验结果表明,该方法不仅提高了图像的对比度,而且在档案库房安防系统的监控中良好的保持了图像的目标信息,提高了后续识别和监控的有效性.     

A new texture image retrieval way

This paper proposes a new texture image retrieval method for the considering of the population search and random information exchange merits of evolving programming which can be used to optimize image feature vector extraction. The experimental results show that this way can efficiently improve the retrieval accuracy and realize fasttips with the advantage of evolving programming algorithm.     

3-D color histogram equalization by principal component analysis

Color histogram equalization is a method for improving visual appearance of images by enhancing image contrast. Color histogram equalization methods are mostly faced with problems like over-enhancement and brightening. In this paper a new color histogram equalization method is proposed which defines a new three dimensional cumulative distribution function based on a one-dimensional histogram. This one-dimensional histogram is calculated by taking into account the correlation between color channels using PCA. Over-enhancement and brightening are solved by this method because of applying the equalization on a transformed image instead of image itself.