A multiresolution framework for local similarity based image denoising

In this paper, we present a generic framework for denoising of images corrupted with additive white Gaussian noise based on the idea of regional similarity. The proposed framework employs a similarity function using the distance between pixels in a multidimensional feature space, whereby multiple feature maps describing various local regional characteristics can be utilized, giving higher weight to pixels having similar regional characteristics. An extension of the proposed framework into a multiresolution setting using wavelets and scale space is presented. It is shown that the resulting multiresolution multilateral (MRM) filtering algorithm not only eliminates the coarse-grain noise but can also faithfully reconstruct anisotropic features, particularly in the presence of high levels of noise.     

Edge based features for content based image retrieval

The common problem in content based image retrieval (CBIR) is selection of features. Image characterization with lesser number of features involving lower computational cost is always desirable. Edge is a strong feature for characterizing an image. This paper presents a robust technique for extracting edge map of an image which is followed by computation of global feature (like fuzzy compactness) using gray level as well as shape information of the edge map. Unlike other existing techniques it does not require pre segmentation for the computation of features. This algorithm is also computationally attractive as it computes different features with limited number of selected pixels.     

A multiresolution manifold distance for invariant image similarity

Accounting for spatial image transformations is a requirement for multimedia problems such as video classification and retrieval, face/object recognition or the creation of image mosaics from video sequences. We analyze a transformation invariant metric recently proposed in the machine learning literature to measure the distance between image manifolds - the tangent distance (TD) - and show that it is closely related to alignment techniques from the motion analysis literature. Exposing these relationships results in benefits for the two domains. On one hand, it allows leveraging on the knowledge acquired in the alignment literature to build better classifiers. On the other, it provides a new interpretation of alignment techniques as one component of a decomposition that has interesting properties for the classification of video. In particular, we embed the TD into a multiresolution framework that makes it significantly less prone to local minima. The new metric - multiresolution tangent distance (MRTD) - can be easily combined with robust estimation procedures, and exhibits significantly higher invariance to image transformations than the TD and the Euclidean distance (ED). For classification, this translates into significant improvements in face recognition accuracy. For video characterization, it leads to a decomposition of image dissimilarity into "differences due to camera motion" plus "differences due to scene activity" that is useful for classification. Experimental results on a movie database indicate that the distance could be used as a basis for the extraction of semantic primitives such as action and romance.     

基于旋转不变纹理特征的多尺度多方向图像渐进检索

纹理检索是基于内容图像检索的重要内容,旋转不变纹理图像检索是实现纹理检索的关键途径之一.针对旋转不变纹理图像检索中需要解决的3个关键问题:如何消除旋转影响、如何选择多尺度分析方法以及如何构造和度量纹理特征矢量,本文分别分析了Radon变换和Log-polar变换在消除旋转位移时对频谱的影响,以及NSCT变换和小波变换在不同检索参数下的平均检索性能,在此基础上构造出多尺度多方向纹理变换谱和旋转不变特征矢量,提出一种多尺度多方向旋转不变纹理图像渐进检索方法.这种方法采用了可顾及人类视觉对纹理能量敏感性的相似性度量标准,分别采用旋转位移处理后的NSCT变换域低频子带和高通子带实现纹理图像的粗检索和精细检索.Brodatz标准纹理图像库的检索实验表明,本文提出的利用多尺度多方向纹理变换谱构造旋转不变特征矢量的方法既可获取纹理主方向,同时又能有效地表征纹理细节信息,两级渐进式检索策略与多尺度分析方法相结合,既能提高旋转不变纹理图像检索的查准率,又能保证较高的检索效率.     

A multi-scale and multi-orientation image retrieval method based on rotation-invariant texture features

Texture retrieval is a vital branch of content-based image retrieval.Rotation-invariant texture retrieval plays a key role in texture retrieval.This paper addresses three major issues in rotation-invariant texture retrieval: how to select the texture measurement methods,how to alleviate the influence of rotation for texture retrieval and how to apply the proper multi-scale analysis theory for texture images.First,the spectrum influence between a Radon transform and a Log-polar transform was compared after t...     

Spatial reasoning and similarity retrieval for image database systems based on RS-strings

In this paper, we propose a rotation-invariant spatial knowledge representation called RS-string. Then we present the string generation algorithm to automatically generate RS-strings for segmented pictures. We also propose the spatial reasoning and similarity retrieval algorithms based on RS-strings. The similarity retrieval algorithm is much more flexible than all previous 2D string representations because our approach can consider every possible view of a query picture. Thus the system does not require the user to provide a query picture which must have the same orientation as that of a database picture. Finally, we provide several examples to demonstrate the capabilities of spatial reasoning and similarity retrieval based on the RS-string representation.     

基于互补特征的纹理图像检索

针对互补特征可以有效地改善图像检索系统性能的特点,提出一种在改进Contourlet变换域采用L1能量与广义高斯分布参数特征的纹理图像检索方法。首先,应用改进的方法对方向子带系数进行广义高斯统计建模。然后,分别单独利用各个特征和相应的相似性测度进行检索。最后,基于直接的相似性测度和,采用这两种互补的特征进行检索。实验结果表明,和采用单一特征相比较,互补特征由于充分地反映了图像的结构信息和随机分布信息,从而有效地提高了纹理图像数据库的平均检索率。     

一种基于混合特征核的图像检索方法

为了更准确地描述图像的视觉特征,提高图像检索的查准率与查全率,提出了一种基于混合特征核的图像检索方法.该方法提取图像的颜色、纹理、SIFT特征,引入高斯核函数,建立图像的混合特征核模型,在高维的核空间进行基于核的图像聚类.实验表明,该混合模型与传统多特征融合方法以及单一特征核方法相比,能够更好地表示图像的视觉特征,提高检索的查准率和查全率.     

基于图像特征的植物形态相似度算法

目的 研究不同植物形态之间的相似度是有效区分植物种类或科属的一个重要依据。目前的植物形态相似度计算方法,大多只考虑了植物拓扑结构或者外围轮廓等几何形状方面的相似性,而未涉及叶片颜色、冠层叶片的稠密状态及株型的松散状态等因素。因此,基于植物图像的形状特征和颜色特征,本文提出一种基于图像特征的植物形态相似度计算方法。方法 首先,获取图像的轮廓特征和区域特征。轮廓特征用植物枝条的松散程度表示,具体包括植物的高宽比、轮廓四边形和第1个1级侧枝的高度;区域特征用叶片稠密度表示,计算叶片所占整个包围矩形面积的比例。其次,获取图像的颜色特征,使用基于HSV和YUV颜色空间的颜色直方图,统计图像的颜色分布。最后,利用信息熵分析数据的离散程度,据此确定各部分对应的权重大小,加权得到总体的相似度值。结果 实验在人工采集的数据集上进行,得出松散度、稠密度和颜色对应的权重分别为0.62、0.17和0.21。在此基础上得到的相似度计算结果符合实际,可以有效度量植物之间的相似程度。同时,将提出的算法应用于图像检索,并与常见的5种方法进行比较。实验得出该算法查准率都在0.747 7以上。在同一查准率水平下,相比于其他方法,查全率也都处于较高水平。尤其在相似度阈值大于0.8时,查准率可以达到0.910 8以上。另外,该方法对植物图像缩放不敏感,同类植物的相似度依然接近于1。结论 本文提出的植物形态相似度算法,结合了形状特征和颜色特征,计算结果符合人的视觉感受。与其他方法相比,可以更有效区分植物种类或科属。算法主要适用于背景单一的单株植物图像,可为研究植物形态的相似性提供技术参考。     

Image retrieval using edge based shape similarity with multiresolution enhanced orthogonal polynomials model

Since shape is one of the important low level features of any Content Based Image Retrieval (CBIR) system, this paper proposes a new edge based shape feature representation method with multiresolution enhanced orthogonal polynomials model and morphological operations for effective image retrieval. In the proposed method, initially the orthogonal polynomials model coefficients are computed and reordered into multiresolution subband like structure. Edge image is then obtained by utilizing the two level adaptive thresholds and local maxima of the gradient in horizontal, vertical, diagonal and anti-diagonal directions. The approximate shape boundary of the image is recovered with morphological operations. Then the Pseudo Zernike moment based global shape features, which are invariant to basic geometric transformations, are extracted. The obtained features are termed as global shape feature vector and are used for retrieving similar images with Canberra distance metric. The efficiency of the proposed method is experimented on a subset of standard Corel, Yale and MPEG-7 databases and the results are compared with existing techniques.     

基于颜色、边缘综合特征的图像检索技术研究

在基于内容的图像检索技术(CBIR)中,颜色作为图像的一种重要视觉信息,已得到广泛应用.相对于图像的几何特征而言,颜色具有一定的稳定性,对缩放、平移、旋转具有很强的鲁棒性.目前基于颜色特征的检索技术很多,但也都有各自的局限性.提出一种新的图像检索算法,基于颜色、边缘二种特征的综合来完成图像的检索.在颜色特征中采取了聚类方法优化像素点颜色量化过程.通过加权处理,将基于颜色直方图的检索技术与基于边缘直方图的检索技术相结合,实现图像检索性能的显著提高.     

遗传反馈的多特征图像检索

基于内容的图像检索是随着数字多媒体技术的发展和普及而新兴的一门信息检索技术。针对当前该领域存在的对图像描述不准确、查询精度低以及反馈次数较多的问题,提出一种基于遗传反馈的图像检索算法。该算法以遗传算法和相关反馈为基础,利用多特征进行检索,避免在利用单一特征进行检索时所出现的不同图像具有相同单一特征（颜色、纹理和形状等）的问题,对图像进行多特征描述可以从多个角度对图像进行定义,大大减少了不同图像却具有相同特征的概率。与现有的算法相比,其具有自动调整图像特征权重、较低反馈次数和较高查询精度的特性。实验结果表明,该算法对于旋转、平移和尺度变化具有较强的鲁棒性,同时具有减少反馈次数和较高查询精度的性能。     

Unsupervised image retrieval framework based on rule base system

This paper introduces unsupervised image retrieval framework based on a rule base system. The proposed framework makes use of geometric moments (GMs) for features extraction. The main advantage with the GMs is that image coordinate transformations can be easily expressed and analyzed in terms of the corresponding transformations in the moment space. These features are used to perform the image mining for acquiring clustering knowledge from a large empirical images database. Irrelevance between images of the same cluster is precisely considered in the proposed framework through a relevant feedback phase followed by a novel clustering refinement model. The images and their corresponding classes pass to a rule base algorithm for extracting a set of accurate rules. These rules are pruning and may reduce the dimensionality of the extracted features. The advantage of the proposed framework is reflected in the retrieval process, which is limited to the images in the class of rule matched with the query image features. Experiments show that the proposed model achieves a very good performance in terms of the average precision, recall and retrieval time compared with other models.     

Learning non-metric visual similarity for image retrieval

Measuring visual similarity between two or more instances within a data distribution is a fundamental task in image retrieval. Theoretically, non-metric distances are able to generate a more complex and accurate similarity model than metric distances, provided that the non-linear data distribution is precisely captured by the system. In this work, we explore neural networks models for learning a non-metric similarity function for instance search. We argue that non-metric similarity functions based on neural networks can build a better model of human visual perception than standard metric distances. As our proposed similarity function is differentiable, we explore a real end-to-end trainable approach for image retrieval, i.e. we learn the weights from the input image pixels to the final similarity score. Experimental evaluation shows that non-metric similarity networks are able to learn visual similarities between images and improve performance on top of state-of-the-art image representations, boosting results in standard image retrieval datasets with respect standard metric distances.     

Adaptive tree similarity learning for image retrieval

Learning-enhanced relevance feedback is one of the most promising and active research directions in content-based image retrieval in recent years. However, the existing approaches either require prior knowledge of the data or converge slowly and are thus not coneffective. Motivated by the successful history of optimal adaptive filters, we present a new approach to interactive image retrieval based on an adaptive tree similarity model to solve these difficulties. The proposed tree model is a hierarchical nonlinear Boolean representation of a user query concept. Each path of the tree is a clustering pattern of the feedback samples, which is small enough and local in the feature space that it can be approximated by a linear model nicely. Because of the linearity, the parameters of the similartiy model are better learned by the optimal adaptive filter, which does not require any prior knowledge of the data and supports incremental learning with a fast convergence rate. The proposed approach is simple to implement and achieves better performance than most approaches. To illustrate the performance of the proposed approach, extensive experiments have been carried out on a large heterogeneous image collection with 17,000 images, which render promising results on a wide variety of queries.An early version of part of the system was reported in Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2001.     

Fast gradual matching measure for image retrieval based on visual similarity and spatial relations

In this article, we propose a new method to retrieve images containing a request set of regions. The user is asked to specify a set of regions belonging to a single image. Then this request set of regions is compared to the sets of the regions of the images in the database. We propose a comparison measure that not only evaluates the similarity of regions one to the other, but that also takes into account the spatial configuration of the regions. The spatial structure of the regions is represented by means of fuzzy spatial relations, like horizontal and vertical disposal and connexity. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 711–723, 2006.     

综合纹理特征和语义信息的医学图像检索

提出一种结合图像分块纹理特征和语义信息的医学胸片图像检索方法。同时,介绍了颜色特征提取方法中的颜色相关图算法。据此,实现了一个图像检索原型系统,依据所设计的评价实验,将不同实验的检索结果进行了比较和分析。实验证明,结合图像分块纹理特征和语义信息的检索方法具有较好的检索效果。