An image retrieval method based on spatial distribution of color

Color histogram is now widely used in image retrieval. Color histogram-based image retrieval methods are simple and efficient but without considering the spatial distribution information of the color. To overcome the shortcoming of conventional color histogram-based image retrieval methods, an image retrieval method based on Radon Transform （RT） is proposed. In order to reduce the computational complexity, wavelet decomposition is used to compress image data. Firstly, images are decomposed by Mallat algorithm. The low-frequency components are then projected by RT to generate the spatial color feature. Finally the moment feature matrices which are saved along with original images are obtained. Experimental results show that the RT based retrieval is more accurate and efficient than traditional color histogram-based method in case that there are obvious objects in images. Further more, RT based retrieval runs significantly faster than the traditional color histogram methods.     

基于多尺度相位特征的图像检索方法

在基于内容的图像检索中,一个关键的问题是图像视觉内容的表述。而传统的颜色,形状和纹理特征对于图像内容的表述尚且不够完备。为进一步提高检索准确率,针对人眼视觉特性,该文提出了一种基于多尺度相位特征的图像检索方法。该方法首先采用尺度空间理论得到图像的多尺度描述,然后通过复数可调滤波(complex steerable filtering)提取图像的多尺度相位信息并利用直方图投影获取全局统计的多尺度相位特征。在通用数据库COREL 5000上的实验结果表明,该特征相对经典的颜色特征提高至少5%检索准确率,且能对之提供有效补充。     

基于潜在语义分析和相关反馈的图像检索方法

为了解决传统的CBIR系统中存在的"语义鸿沟"问题,提出一种基于潜在语义索引技术(LSI)和相关反馈技术的图像检索方法.在进行图像检索时,先在HSV空间下提取颜色直方图作为底层视觉特征进行图像检索,然后引入潜在语义索引技术试图将底层特征赋予更高层次的语义含义;并且结合相关反馈技术,通过与用户交互进一步提高检索精度.实验...     

Image retrieval using both color and texture features

In order to improve the retrieval performance of images, this paper proposes an efficient approach for extracting and retrieving color images. The block diagram of our proposed approach to content-based image retrieval (CBIR) is given firstly, and then we introduce three image feature extracting arithmetic including color histogram, edge histogram and edge direction histogram, the histogram Euclidean distance, cosine distance and histogram intersection are used to measure the image level similarity. On the basis of using color and texture features separately, a new method for image retrieval using combined features is proposed. With the test for an image database including 766 general-purpose images and comparison and analysis of performance evaluation for features and similarity measures, our proposed retrieval approach demonstrates a promising performance. Experiment shows that combined features are superior to every single one of the three features in retrieval.     

On texture and image interpolation using Markov models

Markov-type models characterize the correlation among neighboring pixels in an image in many image processing applications. Specifically, a wide-sense Markov model, which is defined in terms of minimum linear mean-square error estimates, is applicable to image restoration, image compression, and texture classification and segmentation. In this work, we address first-order (auto-regressive) wide-sense Markov images with a separable autocorrelation function. We explore the effect of sampling in such images on their statistical features, such as histogram and the autocorrelation function. We show that the first-order wide-sense Markov property is preserved, and use this result to prove that, under mild conditions, the histogram of images that obey this model is invariant under sampling. Furthermore, we develop relations between the statistics of the image and its sampled version, in terms of moments and generating model noise characteristics. Motivated by these results, we propose a new method for texture interpolation, based on an orthogonal decomposition model for textures. In addition, we develop a novel fidelity criterion for texture reconstruction, which is based on the decomposition of an image texture into its deterministic and stochastic components. Experiments with natural texture images, as well as a subjective forced-choice test, demonstrate the advantages of the proposed interpolation method over presently available interpolation methods, both in terms of visual appearance and in terms of our novel fidelity criterion.     

Content-based image retrieval using local visual attention feature

Content-based image retrieval (CBIR) has been an active research topic in the last decade. As one of the promising approaches, salient point based image retrieval has attracted many researchers. However, the related work is usually very time consuming, and some salient points always may not represent the most interesting subset of points for image indexing. Based on fast and performant salient point detector, and the salient point expansion, a novel content-based image retrieval using local visual attention feature is proposed in this paper. Firstly, the salient image points are extracted by using the fast and performant SURF (Speeded-Up Robust Features) detector. Then, the visually significant image points around salient points can be obtained according to the salient point expansion. Finally, the local visual attention feature of visually significant image points, including the weighted color histogram and spatial distribution entropy, are extracted, and the similarity between color images is computed by using the local visual attention feature. Experimental results, including comparisons with the state-of-the-art retrieval systems, demonstrate the effectiveness of our proposal.     

Indexing and encoding based image feature representation with bin overlapped similarity measure for CBIR applications

In Content Based Image Retrieval (CBIR) system, the exhaustive search for a given query image to find the relevant images in the database are non-scalable. In this paper, we propose indexing, coding technique and similarity measure to address the above mentioned problem. We consider the color histogram of the image and its bin values are analyzed to understand the color information in the image. The histogram dimension is reduced by removing trivial bins and only those bins that represent color information significantly are considered. Based on the dimensions of the histogram, it is clustered and indexed. The Golomb–Rice (GR) coding is used to encode the indexed histograms. The Bin Overlapped Similarity Measure (BOSM) is proposed to compute the distance values between query and database image histograms. The performance of proposed approach is evaluated on benchmark datasets and found that the performance of the proposed approach is encouraging.     

Content-based image retrieval using H.264 intra coding features

Efficient multimedia retrieval has become a vital issue because more audio and video data are now available. This paper focuses on content-based image retrieval (CBIR) in the compression domain (CPD). The retrieval features are extracted based on I-frame coding information in H.264. This paper proposes using a local mode histogram as the texture feature to match images and applying the residual coefficients to filter non-confident modes. The geometrical correspondence between two images is also considered. The experimental results show that the proposed method can substantially reduce computational and memory resource consumption, and provides similar performance compared with methods that extract features from decompressed images.     

改进的旋转不变区域纹理谱描述符

针对区域纹理谱描述符不具备旋转不变性的问题,引入了旋转不变算符,对局域纹理谱描述符进行了改进.采用区域纹理谱直方图捕述图像统计纹理特征,采用局部旋转不变量描述不同区域变化特征,然后融合两种特征作为索引并进行图像检索.采用3种不同类型的图像库对本文方法的检索效果进行了详细比较.实验结果表明,本文提出的旋转不变纹理谱描述符...     

A new SVM based emotional classification of image

How high-level emotional representation of art paintings can be inferred from perceptual level features suited for the particular classes (dynamic vs. static classification) is presented. The key points are feature selection and classification. According to the strong relationship between notable lines of image and human sensations, a novel feature vector WLDLV (Weighted Line Direction-Length Vector) is proposed, which includes both orientation and length information of lines in an image. Classification is performed by SVM (Support Vector Machine) and images can be classified into dynamic and static. Experimental results demonstrate the effectiveness and superiority of the algorithm.     

Image Segmentation Based on GrabCut Framework Integrating Multiscale Nonlinear Structure Tensor

In this paper, we propose an interactive color natural image segmentation method. The method integrates color feature with multiscale nonlinear structure tensor texture (MSNST) feature and then uses GrabCut method to obtain the segmentations. The MSNST feature is used to describe the texture feature of an image and integrated into GrabCut framework to overcome the problem of the scale difference of textured images. In addition, we extend the Gaussian Mixture Model (GMM) to MSNST feature and GMM based on MSNST is constructed to describe the energy function so that the texture feature can be suitably integrated into GrabCut framework and fused with the color feature to achieve the more superior image segmentation performance than the original GrabCut method. For easier implementation and more efficient computation, the symmetric KL divergence is chosen to produce the estimates of the tensor statistics instead of the Riemannian structure of the space of tensor. The Conjugate norm was employed using Locality Preserving Projections (LPP) technique as the distance measure in the color space for more discriminating power. An adaptive fusing strategy is presented to effectively adjust the mixing factor so that the color and MSNST texture features are efficiently integrated to achieve more robust segmentation performance. Last, an iteration convergence criterion is proposed to reduce the time of the iteration of GrabCut algorithm dramatically with satisfied segmentation accuracy. Experiments using synthesis texture images and real natural scene images demonstrate the superior performance of our proposed method.