PACS中基于图像内容的检索

基于图像内容的检索（content based image retrieval,CBIR）改进了传统的基于PACS系统中有着广阔的应用需求.对CBIR算法进行分析,并给出常用的易于实现的6种算法实现方式.最后通过具体的试验数据对各算法进行查询时间和效率的分析,得出对称相互熵这种检索方式在实际中可以达到较好的效果.     

Content based image retrieval via a transductive model

Content based image retrieval plays an important role in the management of a large image database. However, the results of state-of-the-art image retrieval approaches are not so satisfactory for the well-known gap between visual features and semantic concepts. Therefore, a novel transductive learning scheme named random walk with restart based method (RWRM) is proposed, consisting of three major components: pre-filtering processing, relevance score calculation, and candidate ranking refinement. Firstly, to deal with the problem of large computation cost involved in a large image database, a pre-filtering processing is utilized to filter out the most irrelevant images while keeping the most relevant images according to the results of a manifold ranking algorithm. Secondly, the relevance between a query image and the remaining images are obtained with respect to the probability density estimation. Finally, a transductive learning model, namely a random walk with restart model, is utilized to refine the ranking taking into account both the pairwise information of unlabeled images and the relevance scores between query image and unlabeled images. Experiments conducted on a typical Corel dataset demonstrate the effectiveness of the proposed scheme.     

Content based image retrieval using motif cooccurrence matrix

We present a new technique for content based image retrieval using motif cooccurrence matrix (MCM). The MCM is derived from the motif transformed image. The whole image is divided into 2×2 pixel grids. Each grid is replaced by a scan motif that minimizes the local gradient while traversing the 2×2 grid forming a motif transformed image. The MCM is then defined as a 3D matrix whose (i,j,k) entry denotes the probability of finding a motif i at a distance k from the motif j in the transformed image. Conceptually, the MCM is quite similar to the color cooccurrence matrix (CCM), however, the retrieval using the MCM is better than the CCM since it captures the third order image statistics in the local neighborhood. Experiments confirm that the use of MCM considerably improves the retrieval performance.     

一种基于小波压缩的图像检索系统的研究

介绍了一种采用ZTE／改进SPIHT算法的图像检索系统的设计方法。ZTE／进SPIHT算法把图像编码成多层比特流,即底层和加强层,这种特性使得该方法在图像检索系统中优于其它压缩方法。底层比特流同时作为缩小图,消除了数据的冗余,而加强层比特流的快速传输性则改善了检索系统的传输性能。     

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.     

Medical image retrieval based on unclean image bags

Traditional content-based image retrieval (CBIR) scheme with assumption of independent individual images in large-scale collections suffers from poor retrieval performance. In medical applications, images usually exist in the form of image bags and each bag includes multiple relevant images of the same perceptual meaning. In this paper, based on these natural image bags, we explore a new scheme to improve the performance of medical image retrieval. It is feasible and efficient to search the bag-based medical image collection by providing a query bag. However, there is a critical problem of noisy images which may present in image bags and severely affect the retrieval performance. A new three-stage solution is proposed to perform the retrieval and handle the noisy images. In stage 1, in order to alleviate the influence of noisy images, we associate each image in the image bags with a relevance degree. In stage 2, a novel similarity aggregation method is proposed to incorporate image relevance and feature importance into the similarity computation process. In stage 3, we obtain the final image relevance in an adaptive way which can consider both image bag similarity and individual image similarity. The experiments demonstrate that the proposed approach can improve the image retrieval performance significantly.     

Microstructure pattern extraction based image retrieval

Computer vision techniques enhanced by the advent of deep learning has become a quintessential part of our day-to-day life. The application of such computer vision techniques in image retrieval can be termed as query based image retrieval process. Conventional methods have limitations such as increased dimensionality, reduced accuracy, high time consumption, and dependence on indexing for retrieval. In order to overcome these limitations, this research work aims to develop a new image retrieval system by developing an image preprocessing mechanism via target prediction technique, which isolates object from the background. Further, a Micro-structure based Pattern Extraction (MPE) technique is implemented to extract the patterns from the preprocessed image, where the diagonal patterns are generated for increasing the accuracy of the retrieval process. Consequently, the Convolutional Neural Network (CNN) is utilized to reduce the dimensionality of the features, and the similarity learning approach is utilized to map the selected features with trained features based on the distance metric. The performance of the proposed system is evaluated by using various measures. Thereby, the efficiency of the proposed technique is ascertained by comparing it with the existing techniques.

     

基于Tri-training的多特征融合图像检索

为了有效地综合利用图像的多种底层特征进行图像检索,提出将Tri-training方法应用于图像检索过程,将图像的颜色、纹理和形状特征进行了有效的融合。分别提取图像的三维量化颜色直方图、方向可控金字塔二值图像投影和仿射不变区域来表示其颜色、纹理和形状特征,并将三种特征的匹配值作为Tri-training分类器的输入对分类器进行训练和测试。实验结果表明,该方法有效利用了图像的多种特征,达到了很好的检索效果。     

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.     

Fast color-spatial feature based image retrieval methods

In this paper, three types of image features are proposed to describe the color and spatial distributions of an image. In these features, the K-means algorithm is adopted to classify all of the pixels in an image into several clusters according to their colors. By measuring the spatial distance among the pixels in a same cluster, the three types of color spatial distribution (CSD) features of the image is obtained. Based on the three types of CSD features, three image retrieval methods are also provided. To accelerate the image retrieval methods, a fast filter is also presented to eliminate most undesired images in advance. A genetic algorithm is also given to decide the most suitable parameters which are used in the proposed image retrieval methods. The proposed image retrieval methods are simple. Moreover, the experiments show that the proposed methods can provide impressive results as well.     

Medical image retrieval based on complexity analysis

Texture is one of the most important visual attributes used in image analysis. It is used in many content-based image retrieval systems, where it allows the identification of a larger number of images from distinct origins. This paper presents a novel approach for image analysis and retrieval based on complexity analysis. The approach consists of a texture segmentation step, performed by complexity analysis through BoxCounting fractal dimension, followed by the estimation of complexity of each computed region by multiscale fractal dimension. Experiments have been performed with MRI database in both pattern recognition and image retrieval contexts. Results show the accuracy of the method and also indicate how the performance changes as the texture segmentation process is altered.     

基于多尺度HOG的草图检索

草图检索是图像处理领域中的重要研究内容。提出了一种将高斯金字塔和局部HOG特征融合的特征提取改进方法,并将其用于草图检索。采用高斯金字塔将图像分解到多尺度空间,在所有尺度上进行兴趣点提取,获得基于兴趣点的多尺度HOG特征。利用图像的多尺度HOG特征集生成视觉词典,最终形成与视觉词典相关的特征描述向量,通过相似度匹配实现草图检索。将该算法与单一尺度下的HOG算法及其他几种算法比较,实验结果表明了其可行性和有效性。     

一种基于区域综合特征的图像检索方法

提出一种基于目标区域的图像检索方法,首先采用颜色聚类的分割方法将图像分割成不同的区域,提取每个区域的颜色、位置、形状等低层特征,然后提出一种相似度计算方法实现图像的相似性度量。为了提高图像检索的准确度,最后采用支持向量机（SVM）的相关反馈算法。实验结果表明,基于目标区域的图像检索效果比基于全局图像特征的检索效果有较好的改善。     

基于LBSVM机器学习的相关反馈图像检索

基于机器学习的相关反馈技术是基于内容的图像检索研究的热点。由于基于SVM的相关反馈技术存在样本数量少,样本正负比例不平衡,反馈准确率低等问题,文中先对Boosting方法进行改进,提出了用先验知识的Boosting方法与SVM结合的短期机器学习相关反馈方法（BSVM）;在此基础上为进一步提高系统反馈速度与准确率,通过保存训练好的分类器和它对应的样本,提出了基于长期机器学习的相关反馈方法（LBSVM）。文中提出的两种方法与其它方法进行了比较实验,结果表明,该方法优于其它方法。     

基于纹理的旋转不变图像检索算法的研究

提出一种基于Gabor变换的旋转不变多尺度广义粗糙度特征向量并结合自适应加权距离进行纹理图像检索的方法。利用图像Gabor分解的幅度谱,依据多尺度空间局部能量分布、Hurst分形指数、方向差别来计算纹理特征向量,最后采用自适应加权的街区距离作为相似性准则。仿真结果表明,该算法对旋转纹理图像取得了很好的检索结果。     

Social image retrieval based on topic diversity

Image search re-ranking is one of the most important approaches to enhance the text-based image search results. Extensive efforts have been dedicated to improve the accuracy and diversity of tag-based image retrieval. However, how to make the top-ranked results relevant and diverse is still a challenging problem. In this paper, we propose a novel method to diversify the retrieval results by latent topic analysis. We first employ NMF (Non-negative Matrix Factorization) Lee and Seung (Nature 401(6755):788–791, 1999) to estimate the initial relevance score to the query q. Then, the initial relevance score is fed into an adaptive multi-feature fusion model to learn the final relevance score. Next, the diversification process is conducted. We group all the images by semantic clustering and estimate the topic distribution of each cluster by topic analysis. The clusters are ranked based on the topic distribution vector and the final retrieval image list is obtained by a greedy selection mechanism based on the estimated relevances. Experimental results on the NUS-Wide dataset show the effectiveness of the proposed approach.

     

基于多中心卷积特征加权的图像检索方法

深度卷积特征能够为图像内容描述提供丰富的语义信息,为了在图像表示中突出对象内容,结合激活映射中较大响应值与对象区域的关系,提出基于多中心卷积特征加权的图像表示方法。首先,通过预训练深度模型提取出图像卷积特征;其次,通过不同通道特征映射求和得到激活映射,并将激活映射中有较大响应值的位置认为是对象的中心;再次,将中心数量作为尺度,结合激活映射中不同位置与中心的距离为对应位置的描述子加权;最后,合并不同中心数量下的图像特征,生成图像表示用于图像检索。与池化卷积（SPoC）算法和跨维度（CroW）算法相比,所提方法能够为图像表示提供尺度信息的同时突出对象内容,并在Holiday、Oxford和Paris图像集中取得了良好的检索结果。