基于颜色和形状的图像检索

提出一种新的基于颜色和形状的图像检索方法.将彩色图像转换到HSV空间将彩色图像转换到HSV空间,同时将检索图像分块,分别计算每个子块图像的颜色信息熵和中心矩;同时利用Harris算法提取检索图像的兴趣点,计算兴趣点环形空间分布的颜色信息熵和中心矩,以此表征图像的形状特征.针对颜色和形状特征进行高斯归一化,分别计算图像颜色和形状的相似度.最后,利用颜色和形状相似度的加权和进行图像检索.试验结果表明本文算法比CCSI算法和SCH算法具有较高的检索率.     

非负矩阵分解耦合视觉词典的图像检索算法

目的解决当前图像检索技术中,图像特征稀疏编码收敛速度慢,以及局部特征空间信息不足易导致检索误差较大等问题,提出一种基于l0稀疏约束非负矩阵分解耦合视觉词典优化的图像检索算法。方法首先,在非负矩阵分解(Non-negative Matrix Factorization,NMF)的基础上,对系数矩阵设置l0个约束来限制其稀疏性,从而定义一种l0稀疏约束的NMF方法。再通过一种自适应序列词典初始化方案,从训练样本获得词典的初始估计。然后,利用l0稀疏约束的NMF来增强视觉词典,对图像局部描述符进行稀疏编码,并利用最大池化操作来生成聚合特征向量,从而保留局部描述符的关键属性。最后根据得到的特征向量,引入Minkowski距离来衡量查询图像与数据库的相似性,输出检索图像。结果实验结果表明,与当前图像检索方案相比,所提算法具有更高的查准-查全率和收敛速度。结论所提算法返回的图像与查询图像相似度高,在包装商标检索等领域具有一定的参考价值。     

基于结构相似度的彩色图像质量评价算法

目的研究在Matlab中度量彩色图像之间结构相似度的评价算法,用以评估彩色图像的质量。方法以灰度图像的结构相似度评价模型(SSIM)为基础,在Matlab中将以RGB色空间存储的彩色图像转换到亮度信息和色彩信息分别度量的YUV色彩空间中,设计算法度量彩色图像之间的亮度比较值、对比度比较值和结构比较值,综合三部分后可得彩色图像之间的结构相似度。结果实验结果表明,利用彩色结构相似度算法可以计算彩色图像与参考图像之间的相似度数值,用作质量评价,且其评价结果与人类视觉感知评价(即主观评价)结果保持一致。结论该方法可作为一种新型的彩色图像质量评价准则。     

基于自相似性与稀疏表示的超分辨率算法

目的为了解决当前稀疏表示的超分辨率算法效果依赖参与训练的数据的问题,结合图像的自相似性,提出一种基于自相似性与稀疏表示相结合的超分辨率算法。方法算法利用图像的多维自相似性,构建多维图像金字塔,采用改进的相似块搜索策略,得到对应的高低分辨率图像块作为训练样本,然后对样本进行字典训练,最后根据稀疏表示得到超分辨率图像。结果实验结果显示,文中算法在峰值信噪比(PSNR)和结构相似度(SSIM)上优于其他算法,对于实验图像而言,PSNR平均提升了0.5 dB。结论提出的超分辨率算法未引入外部数据库,具有较好的效果,能够用于超分辨率重建。     

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。     

Gabor特征结合Metaface学习的扩展稀疏表示人脸识别

为了克服人脸识别中存在的遮挡等闭塞问题,本文提出了Gabor特征结合Metaface学习的扩展稀疏表示人脸识别算法(GMFL)。考虑到Gabor局部特征对光照、表情和姿态等变化的鲁棒性,该算法首先提取图像的Gabor特征集;然后对Gabor特征集进行Metaface字典学习得到具有更强稀疏表示能力的新字典,同时引入Gabor闭塞字典来编码表示图像中的闭塞部分,并与新字典联合构造一组过完备字典基;最后利用过完备字典基求解稀疏系数重构样本,根据样本与重构样本之间的残差最小原则对人脸图像进行分类识别。在AR人脸库和FERET数据库上的实验结果验证了本文算法的可行性和有效性。     

傅里叶变换耦合曲率尺度空间的图形检索算法

目的解决当前基于曲率尺度空间CSS的图形检索算法仅仅使用了曲率空间图的峰值,且该峰值数量是根据图像形状不断变化的,加上其忽略了图形的重要特征,导致较低的检索精度与效率的不足。方法提出了2D傅里叶变换耦合改进的曲率尺度空间的图形检索算法。首先,考虑零交叉点过程中的曲率动态变化,并定义峰值阀值控制准则,联合抛物线拟合技术,改进了CSS机制,去除伪峰值点,且能兼顾图像形状上的非峰值点信息,获取CSS抛物线拟合图;引入2D傅里叶变换,用CSS抛物线拟合图代替图像形状,获取曲率尺度图的2D傅里叶变换;最后,对其进行归一化,建立曲率-傅里叶描述符,构建查询图形与图形库的欧式距离,完成图形检索。结果 MPEG数据库测试结果显示:与当前利用曲率尺度空间、1D傅里叶描述符的图形检索技术相比,本算法拥有更高的检索精度与效率,呈现出较好的PR曲线。结论所提算法能够进一步提高图形检索精度与效率,在包装商标检索等领域具有较好的应用价值。     

基于多层次视觉语义特征融合的图像检索算法

目的为了解决低层特征与中层语义属性间出现的语义鸿沟,以及在将低层特征转化为语义属性的过程中易丢失信息,从而会降低检索精度等问题,设计一种多层次视觉语义特征融合的图像检索算法。方法首先分别提取图像的3种中层特征(深度卷积神经网络(DCNN)特征、Fisher向量、稀疏编码空间金字塔匹配特征(SCSPM));其次,为了对3种特征进行有效融合,定义一种基于图的半监督学习模型,将提取的3个中层特征进行融合,形成一个多层次视觉语义特征,有效结合3种不同中层特征的互补信息,提高图像特征描述,从而降低检索算法中的语义鸿沟;最后,引入具有视觉特性与语义统一的距离函数,根据提取的多层次视觉语义特征来计算查询图像和训练图像的相似度量,完成图像检索任务。结果实验结果表明,与当前检索方法对比,文中算法具有更高的检索精度与效率。结论所提算法具有良好的检索准确度,在医疗、包装商标等领域具有一定的参考价值。     

基于集合经验模式分解和K-奇异值分解字典学习的滚动轴承故障诊断

针对经典K-奇异值分解算法构造的字典中原子形态受噪声、谐波干扰影响,进而降低冲击故障特征提取精度的问题,提出了基于集合经验模式分解和K-奇异值分解字典学习的冲击特征提取方法。该方法首先利用集合经验模式分解与Hurst指数对振动信号进行预处理,剔除谐波干扰;其次,利用经典K-奇异值分解算法和预处理信号构造超完备字典;然后,利用K-均值聚类算法对字典中的原子进行筛选;最后,利用正交匹配追踪算法实现冲击故障特征的稀疏表示。实验分析和工程应用验证了所提方法的有效性和实用性。     

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。     

基于多尺度特征变换与颜色相关性的商标检索算法

目的 提出一种快速有效的商标注册相似性检查方法,以解决当前基于SIFT的商标检索系统易出现漏检、误检,导致检索精度不高的问题。方法 首先,利用SIFT进行尺度空间创建,并检测商标的特征关键点,通过确定关键点的主方向,可得到具有旋转、缩放、平移、视图变化不变性的图像形状特征描述符。随后,根据像素与其邻域的颜色和空间位置,定义一种改进的颜色相关性,为了有效避免不同商标可能具有相似的颜色特征,对不同的颜色赋予一个权重因子,从而得到一个反映颜色空间相关性与颜色排布疏密度的颜色特征。然后,将SIFT与颜色相关特征向量进行加权组合,并根据实际过程中占主导作用的特征来改变权重。最后,根据加权组合特征,引入马氏距离对查询商标与数据库商标进行相似度量,输出检索商标。结果 实验结果表明,与当前先进的商标检索系统对比,所提算法具有更高的检索准确性与效率。结论 所提算法具有良好的检索准确率与鲁棒性,在商标注册等领域具有一定的实用价值。     

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.     

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。     

用于彩色图像分割的改进遗传FCM算法

本文提出了一种适用于彩色图像分割的遗传模糊C均值聚类(GAFCM)算法.该算法使用Ohta等人提出的彩色特征集中的第一个分量作为图像像素的一维特征向量,并利用由像素空间到特征空间的映射来改进目标函数,从而大大降低了运算量;使用对特征空间结构没有特殊要求的特征距离代替欧氏距离,从而克服了特征空间结构对聚类结果的影响;使用引入FCM优化的遗传算法来搜索最优解,从而提高了搜索速度.实验表明,该算法不但能很好地分割彩色图像,而且具有运算量小、收敛速度快的优点.     

自然场景图像的字符识别方法

目的基于大津算法(Otsu算法)对图像进行分割,利用光学字符识别方法对自然场景图像中的英文字符进行识别。方法首先用分块Otsu算法对图像进行初步的二值化,然后通过对二值化结果的分析,把原始的输入图片分割成单个字符的子图,再对各子图重新用Otsu算法进行二值化,最后对最终得到的二值化结果进行识别,再结合之前得到的每幅图的字符数量信息和词典信息,对识别结果进行修正,得到最终的识别结果。结果在ICDAR2013数据集上测试文中算法,单词正确识别率为46.03%,总编辑距离为474.5。结论文中提出的以Otsu为基础的分块识别算法,能够更好地分割复杂背景图像的背景和文本,同时结合词典信息对识别结果进行了修正,改善了识别效果。     

Distance measures for medical image retrieval

Imaging has occupied a huge role in the management of patients, whether hospitalized or not. Depending on the patient's clinical problem, a variety of imaging modalities were available for use. Radiology is the branch of medical science dealing with medical imaging. It may use X‐ray machines or other such radiation devices. It also uses techniques that do not involve radiation, such as magnetic resonance imaging (MRI) and ultrasound (US). Commonly used imaging modalities include plain radiography, computed tomography (CT), MRI, US, and nuclear imaging techniques. Each of these modalities has strengths and limitations which dictates its use in diagnosis. The usage of modality for a particular problem must be reviewed with emphasis on method of generating an image with costs, strengths and weaknesses, and associated risks. The reason for image retrieval is due to increase in acquisition of images. Physicians and radiologists feel better while using retrieval techniques for faster remedy in surgery and medicine due to the following reasons: giving details to the patients, searching the present and past records from the larger databases, and giving solutions to them in a faster and more accurate way. Similarity measures are one of the techniques that help us in retrieval of medical images. Similarity measures also termed as distance metrics, which plays an important role in CBIR and CBMIR. They calculate the visual similarities between the query image and images in the database which were ranked by their similarities with the query image. Different similarity measures have different effects in an image retrieval system significantly. So, it is important to find the best distance metrics for CBIR system. In this article, various distance methods were used and then they are compared for effective medical image retrieval. A double‐step approach is followed for effective retrieval. This article describes some easily computable distance measures for medical image retrieval using measures such as probability, mean, standard deviation, skew, energy, and entropy. The distance measures used are Euclidean, Manhattan, Mahalanobis, Canberra, Bray‐Curtis, squared chord, and Squared chi‐squared. Two kind of decision rules precision and accuracy were used for measuring retrieval. A dataset is created using various imaging modalities like CT, MRI, and US images. From the final results, it is very clear that each distance metric with each measures shows different results in retrieval of medical images. It is found that the distance metrics with all the measures shows different precision and recall value calculated from their retrieved medical images. The best retrieval results for Euclidean distance metric is only with probability measure showing 75% of precision and 30% of recall when comparing with other measures. The best retrieval results for Manhattan distance metric is only with mean as a measure giving 50% of precision and 20% of recall when compared its performance with other measures in the retrieval of medical images. The best retrieval results for Mahalanobis distance metric is only with probability as a measure giving 75% of precision and 30% of recall when compared its performance with other measures in the retrieval of medical images. The best retrieval results for Canberra distance metric is only with mean as a measure giving 50% of precision and 20% of recall when compared its performance with other measures in the retrieval of medical images. The best retrieval results for Bray‐Curtis distance metric is only with mean as a measure giving 50% of precision and 20% of recall when compared its performance with other measures in the retrieval of medical images. The best retrieval results for squared‐chord distance metric is only with mean as a measure giving 50% of precision and 20% of recall when compared its performance with other measures in the retrieval of medical images. The best retrieval results for squared chi‐chord distance metric is only with mean as a measure showing 50% of precision and 20% of recall when compared its performance with other measures in the retrieval of medical images. These results indicate that these easily computable similarity distance measures have a wide variety of medical image retrieval applications. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 9–21, 2013     

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.