基于显著点特征多示例学习的图像检索方法

提出了一种基于图像显著点特征进行多示例学习(Multiple-instance learning)的图像检索方法.该方法对图像进行小波分解并跟踪不同尺度小波系数提取图像显著点;然后利用显著点特征进行检索,并在相关反馈中将图像看作多示例包,通过期望最大多样性密度(EM-DD,expectation maximization diverse density)方法进行多示例学习,获得体现图像语义的日标特征.在Corel和SIVAL两个图像库进行实验,结果表明该方法明显提高了检索的准确性.     

一种新的综合颜色和纹理特征的图像检索方法

提出综合HSV颜色直方图和Gabor小波纹理特征进行检索的新方法,既利用颜色特征对图像颜色全局分布的描述,又利用纹理特征对局部空间信息的描述,避免一种特征描述图像的片面性。基于Corel库的检索实验结果表明,该方法可以取得良好的检索效果。     

Feature-guided shape-based image interpolation

A feature-guided image interpolation scheme is presented. It is an effective and improved, shape-based interpolation method used for interpolating image slices in medical applications. The proposed method integrates feature line-segments to guide the shape-based method for better shape interpolation. An automatic method for finding these line segments is given. The proposed feature-guided shape-based method can manage translation, rotation and scaling situations when the slices have similar shapes. It can also interpolate intermediate shapes when the successive slices do not have similar shapes. This method is experimentally evaluated using artificial and real two-dimensional and three-dimensional data. The proposed method generated satisfactory interpolated results in these experiments. We demonstrate the practicality, effectiveness and reproducibility of the proposed method for interpolating medical images.     

A privacy-preserving content-based image retrieval method in cloud environment

In order to protect data privacy, image with sensitive or private information needs to be encrypted before being outsourced to a cloud service provider. However, this causes difficulties in image retrieval and data management. A privacy-preserving content-based image retrieval method based on orthogonal decomposition is proposed in the paper. The image is divided into two different components, for which encryption and feature extraction are executed separately. As a result, cloud server can extract features from an encrypted image directly and compare them with the features of the queried images, so that users can thus obtain the image. Different from other methods, the proposed method has no special requirements to encryption algorithms, which makes it more universal and can be applied in different scenarios. Experimental results prove that the proposed method can achieve better security and better retrieval performance.     

A new approach in content-based image retrieval using fuzzy

Finding an image from a large set of images is an extremely difficult problem. One solution is to label images manually, but this is very expensive, time consuming and infeasible for many applications. Furthermore, the labeling process depends on the semantic accuracy in describing the image. Therefore many Content based Image Retrieval (CBIR) systems are developed to extract low-level features for describing the image content. However, this approach decreases the human interaction with the system due to the semantic gap between low-level features and high-level concepts. In this study we make use of fuzzy logic to improve CBIR by allowing users to express their requirements in words, the natural way of human communication. In our system the image is represented by a Fuzzy Attributed Relational Graph (FARG) that describes each object in the image, its attributes and spatial relation. The texture and color attributes are computed in a way that model the Human Vision System (HSV). We proposed a new approach for graph matching that resemble the human thinking process. The proposed system is evaluated by different users with different perspectives and is found to match users’ satisfaction to a high degree.     

Measuring image edge detector accuracy using realisticallysimulated edges

Edge detector tests using edges derived from the acquisition system's edge spread function are described. The test edges used are the most difficult that could exist in a practical system. The results were significantly different to those obtained by traditional methods, indicating that simpler detectors may suffice for some applications     

一种使用边缘增强技术提高相似图片检索召回率的方法

针对大规模图像分类处理中图像旋转或背景变换导致的配准度较低问题,提出一种基于边缘增强的卷积神经网络图像分类方法。该方法通过VGG19网络模型提取图像特征,并使用余弦相似度进行图像分类判定,利用边缘增强突出图像主体的边缘特征,降低图像旋转或背景变换对VGG19网络分类性能带来的影响。实验证明,该方法可以有效地提高同一主体旋转图像和背景变换图像与原始图像的相似度,适用于各类图像的分类。     

Multiscale medial shape-based analysis of image objects

Medial representation of a three-dimensional (3-D) object or an ensemble of 3-D objects involves capturing the object interior as a locus of medial atoms, each atom being two vectors of equal length joined at the tail at the medial point. Medial representation has a variety of beneficial properties, among the most important of which are 1) its inherent geometry, provides an object-intrinsic coordinate system and thus provides correspondence between instances of the object in and near the object(s); 2) it captures the object interior and is, thus, very suitable for deformation; and 3) it provides the basis for an intuitive object-based multiscale sequence leading to efficiency of segmentation algorithms and trainability of statistical characterizations with limited training sets. As a result of these properties, medial representation is particularly suitable for the following image analysis tasks; how each operates will be described and will be illustrated by results: segmentation of objects and object complexes via deformable models; segmentation of tubular trees, e.g., of blood vessels, by following height ridges of measures of fit of medial atoms to target images; object-based image registration via medial loci of such blood vessel trees; statistical characterization of shape differences between control and pathological classes of structures. These analysis tasks are made possible by a new form of medial representation called m-reps, which is described.     

A spine X-ray image retrieval system using partial shape matching

In recent years, there has been a rapid increase in the size and number of medical image collections. Thus, the development of appropriate methods for medical information retrieval is especially important. In a large collection of spine X-ray images, maintained by the National Library of Medicine, vertebral boundary shape has been determined to be relevant to pathology of interest. This paper presents an innovative partial shape matching (PSM) technique using dynamic programming (DP) for the retrieval of spine X-ray images. The improved version of this technique called corner-guided DP is introduced. It uses nine landmark boundary points for DP search and improves matching speed by approximately 10 times compared to traditional DP. The retrieval accuracy and processing speed of the retrieval system based on the new corner-guided PSM method are evaluated and included in this paper.     

Dictionary-based color image retrieval using multiset theory

Dictionaries have recently attracted a great deal of interest as a new powerful representation scheme that can describe the visual content of an image. Most existing approaches nevertheless, neglect dictionary statistics. In this work, we explore the linguistic and statistical properties of dictionaries in an image retrieval task, representing the dictionary as a multiset. This is extracted by means of the LZW data compressor which encodes the visual patterns of an image. For this reason the image is first quantized and then transformed into a 1D string of characters. Based on the multiset notion we also introduce the Normalized Multiset Distance (NMD), as a new dictionary-based dissimilarity measure which enables the user to retrieve images with similar content to a given query. Experimental results demonstrate a significant improvement in retrieval performance compared to related dictionary-based techniques or to several other image indexing methods that utilize classical low-level image features.     

点源离焦图像位相复原方法

为了实现利用点源传递函数测试仪器在测量可见光镜头光学传递函数的同时测出镜头出瞳的波前,采用了一种基于点源目标离焦图像的位相复原算法.该方法无需改变点源传递函数测试仪器的硬件设置,直接利用在传递函数测试过程中,通过改变位于镜头后焦面附近的显微镜与CCD的位置所采集的多幅离焦图像及改进的GS算法实现离焦面与镜头出瞳面光场的...     

A new method for image retrieval based on analyzing fractal coding characters

It has been effectively proved that histogram of image fractal coding parameters can be used for image retrieval. In recent years, many researchers have paid more and more attention to this application of image fractal coding. In this paper, a new statistical method, based on kernel density estimation, is used for analyzing fractal coding parameters. The fractal signatures are then extracted for texture image retrieval. Experimental results show that the proposed method not only has higher retrieval rate but also faster retrieval speed than existing method.     

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.