综合颜色和局部空间特征的彩色图像检索方法

针对图像颜色空间的分布特性,提出一种用颜色色调直方图来表达图像的颜色特征;通过HSV颜色空间的三个独立分量表示方法来描述颜色的空间特征,具有旋转、平移、尺度不变特性。实验表明,该方法很好地克服了颜色直方图和颜色矩无法表达图像色彩的空间位置的缺点,显示了组合特征的图像检索特征的有效性和较高的检索准确性。     

综合颜色和局部空间特征的彩色图像检索方法

针对图像颜色空间的分布特性,提出一种用颜色色调直方图来表达图像的颜色特征;通过HSV颜色空间的三个独立分量表示方法来描述颜色的空间特征,具有旋转、平移、尺度不变特性.实验表明,该方法很好地克服了颜色直方图和颜色矩无法表达图像色彩的空间位置的缺点,显示了组合特征的图像检索特征的有效性和较高的检索准确性.     

基于颜色和边缘信息分布的图像检索

提出了一种基于颜色和形状的图像检索方法。该方法首先将图像非等间隔量化到HSV颜色空间,在用SOBEL算子提取图像的梯度图(高频信息)后,根据HSV颜色聚合块,对梯度图进行必要的修剪,最后用不变矩来描述图像的形状特征;而颜色信息则用HSV颜色空间下的颜色直方图来表示。实验结果表明,该算法有较高的查准率,相比于基本颜色块的半径和角度直方图和普通颜色量化直方图,平均查准率分别提高了12.5%和6.6%。     

基于直方图和模糊C均值的彩色图像分割方法

提出一种基于HSV空间的直方图和模糊C均值(FCM)相结合的彩色图像分割算法.首先将彩色图像转化到HSV空间,考虑到该空间的奇异性,把图像中的像素点根据饱和度和亮度划分为奇异点和非奇异点,然后对非奇异点建立3D HSV颜色直方图,并用爬山算法筛选出峰值进行像素点FCM聚类,对奇异点则建立1D灰度直方图,筛选出峰值进行直方图FCM聚类,最后合并两种分割结果.实验结果表明,该方法对彩色图像能够有效地提取目标物体,具有一定鲁棒性.     

基于颜色和形状的彩色图像检索

采用在HSV彩色空间的色调累积直方图和边缘直方图分别表示颜色和形状特征,进行相似性检索,并结合综合权重调整的相关反馈技术来满足用户的检索需求。实验结果表明,此算法能获得有效的检索效果。     

基于视觉感知特性的颜色直方图

对颜色视觉感知特性进行了研究,利用HSV空间和Weber-Fechner定律分别讨论了颜色的亮度、色调和饱和度三个分量对主观感知的影响,综合这三方面的影响构造了一种颜色主观信息量函数。将此函数应用于直方图提取,得到一种基于视觉感知特性的颜色直方图。对比试验证明该直方图能稳定提升图像检索性能。     

基于颜色和形状特征的彩色图像检索方法

针对基于内容的彩色图像相似性检索问题，提出了一种新的相似性彩色图像检索方法，该方法既考虑了图像的颜色特征，也考虑了图像的空间信息特征，即在对HSV颜色模型进行特殊处理的基础上，将提取的色调不变量作为图像的颜色特征，同时设计了图像状态矩阵来描述图像的形状信息和空间位置信息。在进行图像间的相似性测量时，为了结合不同的子特征进行全局的相似性检索，还采用Guassian模型对不同子特征间的距离进行了归一化处理。用不同类型的图像对这种方法进行的试验结果表明，它用于图像的相似性检索是很有效的，并具有较高的检索效率。     

融合颜色词袋特征的视觉词汇树图像检索

针对由图像灰度空间产生的传统词袋模型SIFT特征无法体现图像的颜色信息的问题,提出了一种融合颜色特征的视觉词汇树来对图像进行描述。提取SIFT特征并建立词汇树,获取图像的SIFT表示向量。利用K-means方法对图像库中的所有图像的HSV值进行聚类,获得基于HSV空间的颜色词袋表示向量,避免了传统颜色直方图方法所带来的量化误差。将SIFT特征与颜色词袋特征进行融合,完成了图像的全局特征和局部特征的融合。然后,计算融合特征的相似度,将相似度从高到低排序,完成图像检索。为了验证本方法的有效性,选择Corel图像库对算法性能进行实验分析,从主观评价和客观评价标准分别进行评价,并与传统方法进行了对比。结果表明,特征融合的检索性能与单一特征方法相比有较大提高。特征融合方法的平均检索查准率和查全率-查准率等评价指标,对比传统方法均有不同程度提高。     

基于颜色和梯度方向共生直方图的图像检索

为有效融合图像的形状、颜色等特征,提出一种基元梯度方向共生直方图和颜色直方图的彩色图像检索算法。首先将图像划分为4×4互不重叠的基元,并根据基元的梯度平均幅值把基元分为视觉均衡基元和视觉非均衡基元;接着计算视觉均衡基元的平均颜色值,并将其在HSV空间中量化到72色颜色空间,构建视觉均衡基元颜色直方图作为彩色图像的局部颜色描述子;然后将图像中各像素点颜色值在HSV空间量化到72色颜色空间,获得图像的全局颜色直方图;最后在视觉非均衡基元中构建梯度方向共生直方图描述图像的形状特征。融合局部颜色描述子、全局颜色直方图和形状特征构成彩色图像检索特征矢量。实验结果表明,所提算法能够准确描述彩色图像的颜色和形状特征,具有很好的旋转不变性和尺度不变性。相似性度量非常有效,查全率和查准率均有较大提高。     

基于颜色扇形模型的图像检索

全局颜色直方图所描述的是各种色彩在整幅图像中所占的比例.无法体现图像颜色的空间分布情况.在HSV颜色模型的基础上,把图像的色调、饱和度和亮度分量分别在径向和角度两个方向上投影,得到六个投影直方图.以这六个投影直方图的前三阶中心矩作为特征,提出了利用扇形模型进行图像检索的算法.实验结果表明,该算法计算简洁、检索速度快,具有较高的查全率和查准率.     

基于HSV空间改进的多尺度显著性检测

图像显著性特征已被广泛地应用于图像分割、图像检索和图像压缩等领域,针对传统算法耗时较长,易受噪声影响等问题,提出了一种基于HSV色彩空间改进的多尺度显著性检测方法。该方法选择HSV色彩空间的色调、饱和度和亮度作为视觉特征,先通过高斯金字塔分解获得三种尺度的图像序列,然后使用改进的SR算法从三种尺度的图像序列中提出每个特征图,最后将这些特征图进行点对点的平方融合和线性融合。与其它算法的对比实验表明,该方法具有较好的检测效果和鲁棒性,能够较快速地检测出图像的显著性区域,能够突显整个显著性目标。     

Statistical framework for image retrieval based on multiresolution features and similarity method

The advent of large scale digital image database leads to great challenges in content-based image retrieval (CBIR) method. The CBIR is considered an active area of research; however, it comprises a strong backdrop for new methodologies and system implementations. Hence, many research contributions focus on these techniques to enable higher image retrieval accuracy while preserving the low level computational complexity. This paper proposes a CBIR method, which is based on an efficient combination of multiresolution based color and texture features. This paper considers color autocorrelogram of the hue(H) and saturation(s) components of HSV color space for color features, and value(V) component of HSV color space for texture features. These two image features are extracted by computing co-occurrence matrix at optimum level image, which is the basis for the formation of feature vector. Though the optimum level is constructed based on wavelet transform, which contains a few dominant wavelet coefficients. The efficiency of the proposed system is tested with standard image databases, and the experimental results show that the proposed method achieves better retrieval accuracy at optimum level; moreover, the proposed method is very fast with low computational load. The obtained results are compared with existing techniques such as orthogonal polynomial model, multiresolution with BDIP-BVLC method and GLCM based system, and results reveal that the proposed method outperforms the existing methods.     

一种新的基于颜色和空间特征的图像检索方法

提出了一种新的基于颜色和空间特征的图像检索方法。首先提取色调不变量作为图像的颜色特征,然后设计了图像状态矩阵来描述图像的形状信息和空间位置信息,在进行相似检索时,采用 Guassian模型对不同子特征间的距离进行了归一化处理。试验表明利用该算法提取的图像的颜色和空间特征在进行图像检索时效果显著。     

Visual information retrieval system via content-based approach

In this paper a content-based image retrieval method that can search large image databases efficiently by color, texture, and shape content is proposed. Quantized RGB histograms and the dominant triple (hue, saturation, and value), which are extracted from quantized HSV joint histogram in the local image region, are used for representing global/local color information in the image. Entropy and maximum entry from co-occurrence matrices are used for texture information and edge angle histogram is used for representing shape information. Relevance feedback approach, which has coupled proposed features, is used for obtaining better retrieval accuracy. A new indexing method that supports fast retrieval in large image databases is also presented. Tree structures constructed by k-means algorithm, along with the idea of triangle inequality, eliminate candidate images for similarity calculation between query image and each database image. We find that the proposed method reduces calculation up to average 92.2 percent of the images from direct comparison.     

基于小波变换的真彩图像降噪与增强

适用于带噪声彩色图像增强的方法少,而应用灰度图像的增强方法,不仅增强噪声,而且会产生色彩偏离。基于此,提出在HSV色彩空间上的降噪与增强方法。根据人眼视觉系统对亮度的变换敏感性大于色度和饱和度的变换敏感性,以及图像噪声只对亮度和饱和度产生影响,将真彩图像由RGB空间变换到HSV空间,保持色度通道不变,对亮度通道进行小波变换,直接对J尺度下的低频信息进行动态范围的压缩,对各尺度下的高频信息进行基于贝叶斯估计阈值的降噪处理。对饱和度通道,利用得到的降噪公式进行降噪处理,将图像变换到RGB 空间。仿真实验证实,增强后的真彩图像包含噪声少,色彩基本无偏差,图像动态范围压缩良好。     

一种新的低照度彩色图像增强算法*

为了增强彩色图像而不引起色彩失真,在HSV颜色空间中保持色相不变,提出了采用分段对数变换增强饱和度结合在多尺度Retinex算法的基础上,采用边缘保持增强色调的低照度彩色图像增强算法。实验结果表明,该方法在保持图像色相和图像边缘的情况下,显著改善了图像的视觉效果,提高了图像的亮度和对比度。25幅低照度图像的平均亮度、标准偏差和对比度分别提高了94.95%、20.93%和29.88%,相对于带色彩恢复的多尺度Retinex算法的熵和对比度增量分别提高了7.34%和151.51%,效果优于Retinex算法。     

Content-Based Image Retrieval Using Multiresolution Color and Texture Features

In this paper, we propose a content-based image retrieval method based on an efficient combination of multiresolution color and texture features. As its color features, color autocorrelograms of the hue and saturation component images in HSV color space are used. As its texture features, BDIP and BVLC moments of the value component image are adopted. The color and texture features are extracted in multiresolution wavelet domain and combined. The dimension of the combined feature vector is determined at a point where the retrieval accuracy becomes saturated. Experimental results show that the proposed method yields higher retrieval accuracy than some conventional methods even though its feature vector dimension is not higher than those of the latter for six test DBs. Especially, it demonstrates more excellent retrieval accuracy for queries and target images of various resolutions. In addition, the proposed method almost always shows performance gain in precision versus recall and in ANMRR over the other methods.      

Extraction of major object features using VQ clustering for content-based image retrieval

An image representation method using vector quantization (VQ) on color and texture is proposed in this paper. The proposed method is also used to retrieve similar images from database systems. The basic idea is a transformation from the raw pixel data to a small set of image regions, which are coherent in color and texture space. A scheme is provided for object-based image retrieval. Features for image retrieval are the three color features (hue, saturation, and value) from the HSV color model and five textural features (ASM, contrast, correlation, variance, and entropy) from the gray-level co-occurrence matrices. Once the features are extracted from an image, eight-dimensional feature vectors represent each pixel in the image. The VQ algorithm is used to rapidly cluster those feature vectors into groups. A representative feature table based on the dominant groups is obtained and used to retrieve similar images according to the object within the image. This method can retrieve similar images even in cases where objects are translated, scaled, and rotated.     

Content-based retrieval of logo and trademarks in unconstrained color image databases using Color Edge Gradient Co-occurrence Histograms

In this paper, we present an algorithm that extends the Color Edge Co-occurrence Histogram (CECH) object detection scheme on compound color objects, for the retrieval of logos and trademarks in unconstrained color image databases. We introduce more accurate information to the CECH, by virtue of incorporating color edge detection using vector order statistics. This produces a more accurate representation of edges in color images, as compared to the simple color pixel difference classification of edges seen with the CECH. Our proposed method is thus reliant on edge gradient information, and so we call it the Color Edge Gradient Co-occurrence Histogram (CEGCH). We also introduce a color quantization method based in the hue–saturation–value (HSV) color space, illustrating that it is a more suitable scheme of quantization for image retrieval, compared to the color quantization scheme introduced with the CECH. Experimental results demonstrate that the CEGCH and the HSV color quantization scheme is insensitive to scaling, rotation, and partial deformations, and outperforms the use of the CECH in image retrieval, with higher precision and recall. We also perform experiments on a closely related algorithm based on the Color Co-occurrence Histogram (CCH) and demonstrate that our algorithm is also superior in comparison, with higher precision and recall.     

综合颜色特征与形状特征的图像检索算法

由于小波变换具有良好的局部特性与多尺度特性,能多尺度逼近边缘,这使得它在图像奇异性检测和特征提取方面得到了广泛的应用。采用二次样条二进小波变换进行边缘检测,用边缘梯度方向直方图表示图像形状特征,用颜色直方图表示图像颜色特征,提出了综合颜色特征和形状特征的图像检索算法。实验结果表明,该算法不仅具有较好的检索性能,而且对图像中存在的光照变化和几何变化（尺度、平移、旋转等）具有较强的鲁棒性。