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.     

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。     

基于熵及不变矩特征的图像检索

提出了一种基于熵及不变矩特征的图像检索算法.图像首先被划分为不同分块,结合图像信息熵的概念,提出采用单元熵来描述分块特征,从而将图像转化为由单元熵构成的熵矩阵;在此基础上,利用不变矩来描述该熵矩阵的特征,并在对该特征归一化的基础上用于图像检索.结合不变矩的特性,试验中对算法的尺度不变性、旋转不变性、平移不变性及对噪声的不敏感性进行了验证,试验结果证明了算法的有效性.同时,由于熵的对称特性,算法对于图像灰度的变化也有较强的鲁棒性.     

On estimation of the number of image principal colors and color reduction through self‐organized neural networks

A new technique suitable for reduction of the number of colors in a color image is presented in this article. It is based on the use of the image Principal Color Components (PCC), which consist of the image color components and additional image components extracted with the use of proper spatial features. The additional spatial features are used to enhance the quality of the final image. First, the principal colors of the image and the principal colors of each PCC are extracted. Three algorithms were developed and tested for this purpose. Using Kohonen self‐organizing feature maps (SOFM) as classifiers, the principal color components of each PCC are obtained and a look‐up table, containing the principal colors of the PCC, is constructed. The final colors are extracted from the look‐up table entries through a SOFM by setting the number of output neurons equal to the number of the principal colors obtained for the original image. To speed up the entire algorithm and reduce memory requirements, a fractal scanning subsampling technique is employed. The method is independent of the color scheme; it is applicable to any type of color images and can be easily modified to accommodate any type of spatial features. Several experimental and comparative results exhibiting the performance of the proposed technique are presented. © 2002 Wiley Periodicals, Inc. Int J Imaging Syst Technol 12, 117–127, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10019     

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.     

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;     

Color reduction for complex document images

A new technique for color reduction of complex document images is presented in this article. It reduces significantly the number of colors of the document image (less than 15 colors in most of the cases) so as to have solid characters and uniform local backgrounds. Therefore, this technique can be used as a preprocessing step by text information extraction applications. Specifically, using the edge map of the document image, a representative set of samples is chosen that constructs a 3D color histogram. Based on these samples in the 3D color space, a relatively large number of colors (usually no more than 100 colors) are obtained by using a simple clustering procedure. The final colors are obtained by applying a mean‐shift based procedure. Also, an edge preserving smoothing filter is used as a preprocessing stage that enhances significantly the quality of the initial image. Experimental results prove the method's capability of producing correctly segmented complex color documents where the character elements can be easily extracted as connected components. © 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 14–26, 2009     

Image retrieval using wavelet-based salient regions

Abstract

A content-based image retrieval system normally returns the retrieval results according to the similarity between features extracted from the query image and candidate images. In certain circumstances, however, users may concern more about salient regions in an image of their interest and only wish to retrieve images containing the relevant salient regions while ignoring those irrelevant (such as the background or other regions and objects). Although how to represent the local image properties is still one of the most active research issues, much previous work on image retrieval does not examine salient regions in an image. In this paper, we propose an improved salient point detector based on wavelet transform; it can extract salient points in an image more accurately. Then salient points are segmented into different salient regions according to their spatial distribution. Colour moments and Gabor features of these different salient regions are computed and form a feature vector to index the image. We test the proposed scheme using a wide range of image samples from the Corel Image Library. The experimental results indicate that the method has produced promising results.     

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。     

Shape,Color and Texture Based CBIR System Using Fuzzy Logic Classifier

The perfect image retrieval and retrieval time are the two major challenges in CBIR systems. To improve the retrieval accuracy, the whole database is searched based on many image characteristics such as color, shape, texture and edge information which leads to more time consumption. This paper presents a new fuzzy based CBIR method, which utilizes colour, shape and texture attributes of the image. Fuzzy rule based system is developed by combining color, shape, and texture feature for enhanced image recovery. In this approach, DWT is used to pull out the texture characteristics and the region based moment invariant is utilized to pull out the shape features of an image. Color similarity and texture attributes are extorted using customized Color Difference Histogram (CDH). The performance evaluation based on precision and BEP measures reveals the superiority of the proposed method over renowned obtainable approaches.     

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。     

基于背景代表色提取的迷彩伪装颜色选取算法

为了达到好的伪装效果,迷彩颜色应与背景色调相融合,以使人眼及光学仪器难以探测和分辨.因此,背景主色的准确提取是确定迷彩颜色的前提.利用灰度直方图可以确定背景图像中的主要灰度,但无法区分不同色调.而基于颜色直方图的背景主色提取方法的运算量太大.本文提出了一种基于HSI模型和量化颜色直方图的迷彩颜色选取算法.利用HSI颜色模型描述背景颜色特性,通过特殊量化方式对背景的颜色直方图进行量化,接着借助阈值方法选取背景主色作为迷彩颜色.结合迷彩伪装图案设计方法对上述迷彩颜色选取算法进行了实验分析,并通过边缘检测和相关跟踪方法对不同背景下的目标迷彩伪装效果进行了验证.     

CANFIS based glioma brain tumor classification and retrieval system for tumor diagnosis

Brain tumor classification and retrieval system plays an important role in medical field. In this paper, an efficient Glioma Brain Tumor detection and its retrieval system is proposed. The proposed methodology consists of two modules as classification and retrieval. The classification modules are designed using preprocessing, feature extraction and tumor detection techniques using Co‐Active Adaptive Neuro Fuzzy Inference System (CANFIS) classifier. The image enhancement can be achieved using Heuristic histogram equalization technique as preprocessing and further texture features as Local Ternary Pattern (LTP) features and Grey Level Co‐occurrence Matrix (GLCM) features are extracted from the enhanced image. These features are used to classify the brain image into normal and abnormal using CANFIS classifier. The tumor region in abnormal brain image is segmented using normalized graph cut segmentation algorithm. The retrieval module is used to retrieve the similar segmented tumor regions from the dataset for diagnosing the tumor region using Euclidean algorithm. The proposed Glioma Brain tumor classification methodology achieves 97.28% sensitivity, 98.16% specificity and 99.14% accuracy. The proposed retrieval system achieves 97.29% precision and 98.16% recall rate with respect to ground truth images.     

Position‐dependent defocus processing for acoustic holography images

Acoustic holography is a transmission‐based ultrasound imaging method that uses optical image reconstruction and provides a larger field of view than pulse‐echo ultrasound imaging. A focus parameter controls the position of the focal plane along the optical axis, and the images obtained contain defocused content from objects not near the focal plane. Moreover, it is not always possible to bring all objects of interest into simultaneous focus. In this article, digital image processing techniques are presented to (1) identify a “best focused” image from a sequence of images taken with different focus settings and (2) simultaneously focus every pixel in the image through fusion of pixels from different frames in the sequence. Experiments show that the three‐dimensional image information provided by acoustic holography requires position‐dependent filtering for the enhancement step. It is found that filtering in the spatial domain is more computationally efficient than in the frequency domain. In addition, spatial domain processing gives the best performance. © 2002 Wiley Periodicals, Inc. Int J Imaging Syst Technol 12, 101–111, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10017     

基于颜色矢量角度直方图与DCT压缩的鲁棒哈希算法

目的为了改善哈希序列对篡改内容的正确识别率和鲁棒性,提出基于颜色矢量角度直方图耦合离散余弦变换压缩的鲁棒哈希算法。方法结合插值运算和Gaussian滤波器,对图像实施预处理,使其对于任意的认证目标均可产生一个固定长度的哈希序列;引入极坐标变换LPT(Log-polar transform),对规范尺寸图像完成坐标变换,输出二次图像;借助SVD(SingularValueDecompostion)方法来处理二次图像,输出全局鲁棒特征;提取规范尺寸图像中所有像素分量的颜色矢量角度,并计算其对应的直方图,形成色彩感知特征;将全局与色彩感知特征组合,将其视为1D哈希序列;基于离散余弦变换DCT(Discrete Cosine Transform),对1D哈希序列实施压缩,输出一组交流系数;引入Logistic映射,根据其输出的混沌序列来设计差异加密方法,对交流系数实施扩散,从而形成目标哈希序列。基于l2范数距离和优化的识别阈值,对待检测图像内容的真伪实施判别。结果试验结果显示,相对于已有的哈希机制而言,所提方案具备更强的鲁棒性,对各类几何攻击均有更高的检测准确度。结论所提哈希方法具有较高的鲁棒性与识别正确率,对商标检索、信息防伪等领域具有较好的参考价值。     

Color‐edge detection based on discrimination of noticeable color contrasts

Color‐edge detection is an important research task in the field of image processing. Efficient and accurate edge detection will lead to higher performance of subsequent image processing techniques, including image segmentation, object‐based image coding, and image retrieval. To improve the performance of color‐edge detection while considering that human eyes are ultimate receiver of color images, the perceptually insignificant edges should avoid being over‐detected. In this article, a color‐edge detection scheme based on the perceptual color contrast is proposed. The perceptual color contrast is defined as the visible color difference across an edge in the CIE‐Lab color space. A perceptual metric for measuring the visible color difference of a target color pixel is defined by utilizing the associated perceptually indistinguishable region. The perceptually indistinguishable region for each color pixel in the CIE‐Lab color space is estimated by the design of an experiment that considers the local property due to local changes in luminance. Simulation results show that the perceptual color contrast is effectively defined and the color edges in color images are detected while most of the perceptually insignificant edges are successfully suppressed through the proposed color‐edge detection scheme. © 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 332–339, 2009     

Virtual view synthesis method and self‐evaluation metrics for free viewpoint television and 3D video

Virtual view synthesis is one of the most important techniques to realize free viewpoint television and three‐dimensional (3D) video. In this article, we propose a view synthesis method to generate high‐quality intermediate views in such applications and new evaluation metrics named as spatial peak signal‐to‐noise ratio and temporal peak signal‐to‐noise ratio to measure spatial and temporal consistency, respectively. The proposed view synthesis method consists of five major steps: depth preprocessing, depth‐based 3D warping, depth‐based histogram matching, base plus assistant view blending, and depth‐based hole‐filling. The efficiency of the proposed view synthesis method has been verified by evaluating the quality of synthesized images with various metrics such as peak signal‐to‐noise ratio, structural similarity, discrete cosine transform (DCT)‐based video quality metric, and the newly proposed metrics. We have also confirmed that the synthesized images are objectively and subjectively natural. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 378–390, 2010