A content-based image retrieval system based on object-moment feature

The last decade has witnessed great interest in research on content-based image retrieval (CBIR). In 2009, Lin et al. proposed a smart CBIR system based on colour and texture feature. Their system has a high detection rate except the cases where image objects have similar shapes. To enhance the detection rate a shape-based image feature called object-moment is proposed in this paper. Object-moment uses the moment of force to compute the object edge feature by calculating the distance from each edge pixel to the axis, and adding them up as a feature. Besides, we integrate the colour features (NSOM, CSOM) and the texture features (CCM, DBPSP) to enhance image detection rate and simplify computation of image retrieval. A series of analyses and comparisons are performed in our experiments to demonstrate that our proposed method improves the retrieval accuracy significantly.     

Content-based image retrieval using color vector angle difference histogram

Abstract

This paper presents an innovative method to represent an image for image retrieval in Content-Based Image Retrieval (CBIR), using a color vector angle differences histogram approach (CVADH), which is different from the existing histograms, such as color histograms, gray level co-occurrences (GLCM), color distribution entropy (CDE), or just counting the frequency number of pixels or neighbors. In contrast to other methods, the CVADH takes account of the color difference and spatial information of two pixels in RGB color space. Furthermore, it directly leverages color clues to extract shape features to show differences given different backgrounds. Experimental results show that the scheme is more efficient than traditional ones that have been developed for CBIR.     

基于方块编码的图像特征提取及检索算法

提出了一种基于方块编码的图像检索算法.首先将图像分成互不重叠的子图像块,根据图像块中各像素间的色差,利用方块编码的思想对这些子图像进行编码,然后根据人眼的视觉特性来定义图像的关键块,最后借助于基于关键字的文本检索技术进行图像检索.同时,考虑到不同类型的关键块在表征图像内容时重要程度的不同而赋予其不同的权值.实验结果表明本文算法在图像的相似性检索时是有效的,并具有较高的检索效率.     

Feature extraction through generalization of histogram refinement technique for local region‐based object attributes

Content based image retrieval (CBIR) is used to retrieve digital images from large databases. However, the problem of retrieving images on the basis of the contents remains largely unsolved. The proposed method of image retrieval is based on the information provided by histogram analysis of the intensity or grayscale values of images. Some additional properties are also calculated and used that are based on regional characteristics of various objects in the image. The need to retrieve the additional regional properties arises due to the fact that the standard histograms are insensitive to small changes in images. Many images of different types can have similar histograms, because, histograms provide only a coarse characterization of an image. This is the main disadvantage of using histograms. This research is based on the concept of Histogram Refinement (Pass and Zabih, IEEE Workshop Appl Comput Vision ( 1996 ), 96–102). Distributing the grayscale image intensities by splitting the pixels using their intensity values into several classes just like the histogram refinement method can provide an estimate of the object characteristics present in an image. After the calculation of clusters using a color refinement method, the inherent features of each of the clusters is calculated based on the regional properties of the clusters. These additional region based features expound some structural information of the image. Finally, all of these features are used for image retrieval. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 298–306, 2011;     

A Privacy-Preserving Image Retrieval Based on AC-Coefficients and Color Histograms in Cloud Environment

Content based image retrieval (CBIR) techniques have been widely deployed in many applications for seeking the abundant information existed in images. Due to large amounts of storage and computational requirements of CBIR, outsourcing image search work to the cloud provider becomes a very attractive option for many owners with small devices. However, owing to the private content contained in images, directly outsourcing retrieval work to the cloud provider apparently bring about privacy problem, so the images should be protected carefully before outsourcing. This paper presents a secure retrieval scheme for the encrypted images in the YUV color space. With this scheme, the discrete cosine transform (DCT) is performed on the Y component. The resulting DC coefficients are encrypted with stream cipher technology and the resulting AC coefficients as well as other two color components are encrypted with value permutation and position scrambling. Then the image owner transmits the encrypted images to the cloud server. When receiving a query trapdoor form on query user, the server extracts AC-coefficients histogram from the encrypted Y component and extracts two color histograms from the other two color components. The similarity between query trapdoor and database image is measured by calculating the Manhattan distance of their respective histograms. Finally, the encrypted images closest to the query image are returned to the query user.     

A colour image retrieval scheme based on Z-scanning technique

Abstract

This paper proposes a new colour image retrieval scheme using Z-scanning technique for content-based image retrieval (CBIR). In recent years, the CBIR is a popular research topic for image retrieval. This paper proposes a scheme which employs the Z-scanning technique to extract directional intensity features for measuring the similarity between query and database images. In the multiple channel images, each colour channel can be processed individually or combined into a grey channel Y. In order to extract the features by Z-scanning technique from all images, each channel of all images must be divided into several N×N blocks. In each block, F pairs of pixels are scanned by a ‘Z’ direction to obtain the texture features. Each colour channel can be obtained an M×M Z-scanning co-occurrence matrix (ZSCM) for storing the probability of each relationship of all closest blocks. At the similarity measure stage, the ZSCMs of query image and database images are compared to measure their similarity. The experimental results show that the proposed scheme is beneficial for image retrieval when the images include the same texture or object. On the other hand, the proposed scheme also can get better retrieval results and more efficiency than colour correlogram (CC) technique for colour texture images. Another technique uses motif co-occurrence matrix (MCM) as the feature in similarity measurement. The experimental results show the proposed ZSCM can get better retrieval results and higher recall and precision values than the CC and MCM techniques for public image databases.     

基于包装外观特征图像内容检索方法研究

针对包装外观特征图像信息在图像数据特征的表述、图像内容的检索方法上进行了阐述,并对图像中的颜色特征、纹理特征、轮廓特征进行了表述分析,以便于图像特征的提取和图像内容的匹配,从而实现图像内容的检索,为图像数据库的储存、检索、处理提供相应的处理技术。     

基于泽尼克矩耦合颜色空间加权度量的商标检索算法

目的 针对商标检索系统中利用单一特征进行识别和度量时,往往难以充分表征商标特征,易出现检索精度和鲁棒性不高等问题,文中拟设计一种泽尼克（Zernike）矩耦合颜色空间加权度量的商标检索方案。方法 首先,利用Zernike矩作为商标的形状描述符,充分描述商标的形状信息。随后,利用颜色空间来描述图像中像素空间信息的颜色分布特征。然后,分别将输入商标的Zernike矩特征、颜色空间特征与存储在数据库中的特征进行匹配,以计算Zernike矩特征的加权Euclidean距离与颜色空间度量。最后,联合颜色空间度量与Euclidean距离,综合考虑形状与颜色特征,形成新的距离测量规则,输出与查询商标相似的商标。结果 实验数据表明,与当前商标检索算法相比较,所提算法具有更高的检索准确率与鲁棒性,表现出更为理想的Precision-Recall以及平均准确率（Mean Average Precision, MAP）。结论 所提算法返回的图像与查询图像相似度较高,在商标注册、侵权保护等方面中具有一定的参考价值。     

结合稀疏表示和子空间投影的云图检索

卫星云图能从多角度展示各类云系特征及其演变过程,实现基于内容的云图检索在天气实况监测、气候研究等方面具有重要意义。为了优化云图的组合特征,增强其组合特征的泛化能力,本文提出一种结合稀疏表示和子空间投影的特征优化方法。首先分别提取云图的颜色、纹理以及形状三种特征,并对其组合特征进行转换分块;然后对每一块的特征进行稀疏表示,根据不同原子的方差来分组特征,得到显著特征和非显著特征;最后由分组特征的能量来计算得到子空间投影矩阵,将初始的组合特征在投影矩阵上进行投影,得到优化后的云图特征。实验结果表明,本文优化云图特征的方法在查准率、查全率上均优于常用的降维方法和云图检索技术,对组合特征具有较强的优化能力,在实时检索过程中时间复杂度低,是一种全新的检索方法。     

一种新的基于颜色矢量角的图像空间描述符

在分析利用颜色直方图进行图像检索时存在问题的基础上,提出了一种新的图像空间描述符.首先利用颜色矢量角的特性将图像分为边缘区和平滑区两部分,然后,针对两种区域的不同分布特点,综合利用图像的边缘信息、形状信息及空间分布特性,采用两种不同的描述符--方向颜色矢量角直方图和空间联合分布熵分别来提取图像的边缘区和平滑区的分布特征.实验结果表明,将其用于图像检索,取得了很好的检索效果.     

基于双级特征提取与度量的图像检索算法

目的 针对单一低层特征在语义属性中的信息易丢失,导致其对图像描述能力不强,使其检索精度不佳的问题,结合颜色矩（CM）、角径向变换描述符（ART）和边缘直方图（EH）等3种特征,定义一种双级特征提取与度量的图像检索方案。方法 首先,将图像转换为HSV色彩空间,并将其分割为若干个非重叠子图像,通通过计算每个子图像的均值、标准差和偏斜度来表征CM;再利用Euclidean距离,对查询图像和数据库图像的CM进行提取与度量,将输出的检索结果标记为一个图像集。随后,提取查询图像与图像集中每个目标的ART和EH特征;利用Euclidean距离分别度量查询图像与图像集中目标的ART与EH的相似性;最后,对ART与EH的加权组合,输出相似性最高的检索图像。结果 实验表明,与当前常见的检索算法比较,文中算法具有更高的检索精度,表现出更优异的Precision-Recall曲线。结论 所提算法具有良好的检索准确度,在信息处理、包装商标等领域具有一定的参考价值。     

一种快速图像纹理分析算法

针对目前的纹理分析算法普遍存在计算复杂、运行时间过长的缺点,提出了一种新的图像纹理分析算法。该算法利用差分概念定义出图像的差分矩阵和差分直方图,并从差分直方图中提取特征进行图像纹理的相似度度量。由于本算法在考虑人类视觉特性的基础上尽量采用加减运算,因此不但具有与人类视觉特性相联系的特点,更使得算法的运行效率大大提高。使用本算法对brodatz 图像库进行的检索实验表明,该算法在查准率和检索效率上与共生矩阵算法相比分别提高13.9160%和 9.5 倍,具有与传统算法相比更好的检索效果和更高的检索效率。因此该算法可以满足实时性要求较高的场合,弥补了目前算法运行时间长的缺点。     

基于区域生长耦合多分类器的商标检索算法

目的针对商标检索算法中易出现的语义鸿沟,底层视觉特征与高层语义相关性不强而导致商标检索精度不理想的问题,定义一种基于区域生长耦合多分类器的商标检索方案。方法首先对输入的商标进行预处理,去除图像中的噪声和杂散点,并通过3D直方图和聚类算法来提取输入图像中的主颜色;基于区域生长算法,合并具有相同颜色标签的所有连接点,以形成颜色区域;然后根据生成的颜色区域,分别定义颜色分类器、形状分类器和关系分类器,利用每个分类器计算查询图像和数据库中图像的检索优势概率;最后通过决策组合,根据检索规则和列表长度找到最相似的商标,并利用动态选择方案进一步提高检索准确率。结果实验结果表明,与当前商标检索方案相比,所提检索系统具有更为理想的Precision-Recall曲线,对缩放、扭曲和噪声具有更高的鲁棒性。结论所提方案在各类几何变换下具备较高的检索准确率,对商标注册、版权保护等行业有较好的借鉴意义。     

基于字典统计耦合归一化多重距离的图像检索算法

目的针对当前彩色图像检索技术容易受到色彩的干扰,使其鲁棒性不强等不足,提出一种字典统计耦合归一化多重距离的彩色图像检索算法。方法首先将图像量化并转换成一维信号,然后引入字典统计,将一维信号进行字典编码,并计算编码后的图像多样值,在归一化字典距离的基础上嵌入字典编码图像的多样值,从而定义归一化多重距离(NMD)的相似度量准则,利用NMD对查询图像与数据库图像的多样值进行比较与识别,搜索出与查询图像具有相同特征的最相似图像,完成目标检索。结果在COREL数据库的实验结果表明,相对于当前常用的检索技术,该检索算法具有更高的查准率和查全率,可对彩色图像完成精确检索,有效减低了色彩对检索性能的干扰。结论文中算法具有较好的检索精度,能够较好地用于医疗、商标等领域的目标检索。     

基于颜色内容的图像检索方法的比较

通过对基于颜色内容的图像检索技术的研究,提出了几种针对图像颜色内容的图像检索方法及其优化方案,并通过实验,分析和比较了在孟赛尔颜色空间(MTM)和传统HSV颜色空间下,一般直方图和累加直方图检索算法的性能和优劣性,所获成果对基于内容的图像检索技术的发展有一定的指导作用.     

旋转不变纹理特征用于两级图像检索

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。粗检中,通过坐标变换把图像的旋转转换为行移,并提取近似行移不变的小波特征,结合粗比较算法对整个图像库进行粗检。然后对通过粗检的图像进行 Gabor 变换,提取旋转不变精检索特征,并使用Canberra 距离进行相似性度量。通过对旋转图像库的测试表明,该方法不仅加快了运算速度,且当参数选择适当时,在相同特征条件下,检索率比直接使用精检索方法检索时还提高了 1.625%。     

基于Camshift的多特征自适应融合船舶跟踪算法

基于航道内船舶监控图像序列的多目标跟踪技术是开展船撞主动预警,提升桥区船舶通航安全的前提.基于颜色直方图的Camshift跟踪算法在复杂气象条件下无法得到准确的跟踪结果,本文提出了一种多特征自适应融合的多目标跟踪算法.该算法的目标模型由颜色、形状及纹理多特征自适应融合实现,增加了描述目标模型的可靠性和鲁棒性;在跟踪目标...     

综合PHOG和LWT的图像检索算法

目的研究物体的形状特征在图像描述及图像检索中的区分度和检索性能。方法设计一种综合PHOG形状和提升小波变换的图像检索算法。算法首先对原始图像进行极坐标系方向归一化,提取图像旋转不变特征;其次提取分层图像的PHOG形状特征;然后提取分层图像低频变换系数均值和方差作为提升小波变换特征;最后将各种特征进行融合并用于图像检索,并定义距离衡量公式。结果通过文中设计算法提取的图像形状特征可使各标准测试图像间距离均值为0.2352。结论在Corel图像集上的检索实验结果优于RIM算法和FWTH算法,表明文中算法图像检索领域具有一定的应用前景。     

Improved cuckoo search with particle swarm optimization for classification of compressed images

The need for a general purpose Content Based Image Retrieval (CBIR) system for huge image databases has attracted information-technology researchers and institutions for CBIR techniques development. These techniques include image feature extraction, segmentation, feature mapping, representation, semantics, indexing and storage, image similarity-distance measurement and retrieval making CBIR system development a challenge. Since medical images are large in size running to megabits of data they are compressed to reduce their size for storage and transmission. This paper investigates medical image retrieval problem for compressed images. An improved image classification algorithm for CBIR is proposed. In the proposed method, RAW images are compressed using Haar wavelet. Features are extracted using Gabor filter and Sobel edge detector. The extracted features are classified using Partial Recurrent Neural Network (PRNN). Since training parameters in Neural Network are NP hard, a hybrid Particle Swarm Optimization (PSO) – Cuckoo Search algorithm (CS) is proposed to optimize the learning rate of the neural network.     

基于颜色信息与空间特征的自适应商标检索算法

目的为了增强商标检索技术对商标特征的描述能力,改善其在外来干扰下的检索精度与鲁棒性。方法提出一种基于颜色与空间特征自适应结合的商标检索算法。首先,引入主颜色描述符(DCD),将其作为颜色特征检测器,并在颜色特征提取时嵌入k-均值聚类算子,增强颜色区域,准确提取颜色特征。随后,每个商标被量化为8个显色的最大值,以便提取每个颜色分量中的空间分布信息。然后,通过利用不同的权重来平衡颜色与空间特征的重要性,定义一种基于模糊直方图分析技术,计算每个商标自适应系数,以准确描述彩色商标的图像特征。最后,通过Euclidean距离进行相似度量,输出检索到的商标。结果实验结果表明,与当前商标检索方法相比,所提算法具有更高的检索精度与鲁棒性,呈现出更理想的P-R曲线,在召回率为0.7时,其检索准确率仍可达到90%。结论文中检索方法具有较高的检索精度,在包装商标检测、商标版权保护等领域中具有良好的应用价值。