融合词袋连通图的图像检索特征选择

目的 随着图像检索所依赖的特征愈发精细化,在提高检索精度的同时,也不可避免地产生众多非相关和冗余的特征。针对在大规模图像检索和分类中高维度特征所带来的时间和空间挑战,从减少特征数量这一简单思路出发,提出了一种有效的连通图特征点选择方法,探寻图像检索精度和特征选择间的平衡。方法 基于词袋模型（bag of words,BOW）的图像检索机制,结合最近邻单词交叉核、特征距离和特征尺度等属性,构建包含若干个连通分支和平凡图的像素级特征分离图,利用子图特征点的逆文本频率修正边权值,从各连通分量的节点数量和孤立点最近邻单词相关性两个方面开展特征选择,将问题转化为在保证图像匹配精度情况下,最小化特征分离图的阶。结果 实验采用Oxford和Paris公开数据集,在特征存储容量、时间复杂度集和检索精度等方面进行评估,并对不同特征抽取和选择方法进行了对比。实验结果表明选择后的特征数量和存储容量有效约简50%以上;100 k词典的KD-Tree查询时间减少近58%;相对于其他编码方法和全连接层特征,Oxford数据集检索精度平均提升近7.5%;Paris数据集中检索精度平均高于其他编码方法4%,但检索效果不如全连接层特征。大量实验表明了大连通域的冗余性和孤立点的可选择性。结论 通过构建特征分离图,摒弃大连通域的冗余特征点,保留具有最近邻单词相关性的孤立特征点,最终形成图像的精简特征点集。整体检索效果稳定,其检索精度基本与原始特征点集持平,且部分类别效果优于原始特征和其他方法。同时,选择后特征的重用性好,方便进一步聚合集成。     

Color and texture applied to a signature-based bag of visual words method for image retrieval

Multimedia Tools and Applications - This article addresses the problem of representation, indexing and retrieval of images through the signature-based bag of visual words (S-BoVW) paradigm, which...     

HEp-2 image classification using intensity order pooling based features and bag of words

Human Epithelial type 2 (HEp-2) cells play an important role in the diagnosis of autoimmune disorder. Traditional approach relies on specialists to observe HEp-2 slides via the fluorescence microscope, which suffers from a number of shortcomings like being subjective and labor intensive. Pattern recognition techniques have been recently introduced to this research issue to make the process automatic. However, performances of current systems available in literature are not satisfying. We propose in this paper a framework using intensity order pooling based gradient feature and bag of words for HEp-2 classification. By pooling the gradient features based on the intensity orders of local grid points, the pooled feature is rotationally invariant without requirement of orientation estimation. The proposed approach was fully tested using publicly available ICPR dataset and our own SZU dataset. Experimental results show that the propose method significantly outperformed widely used SIFT feature and the winner of ICPR contest 2012. Encouraging 100% image level accuracy was achieved on the SZU dataset.     

一种基于优化“词袋”模型的物体识别方法*

针对传统基于“词袋”模型物体识别现有方法的不足,对现特征表达、视觉词典和图像表示方法进行优化,以提高物体识别正确率。采用HUE直方图与SIFT特征描述符分别描述兴趣点周围的颜色和形状特征,实现“词袋”模型下两种特征的特征级和图像级融合,引入K-means++聚类算法生成视觉词典,并利用软权重思想将特征向量映射到视觉单词形成图像直方图。实验结果表明,所述方法会产生较高的物体识别正确率,且识别结果不受两种特征融合权重的影响。     

Content-based medical image retrieval using a novel hybrid scattering coefficients - bag of visual words - DWT relevance fusion

Multimedia Tools and Applications - Image content analysis plays a major role in image classification, retrieval, and indexing together with object and scene recognition. Numerous image content...     

Fuzzy approach for image retrieval

Current technology allows the acquisition, transmission, storing, and manipulation of large collections of images. Content-based information retrieval is now a widely investigated issue that aims at allowing users of multimedia information systems to retrieve images coherent with a sample image. A way to achieve this goal is the automatic computation of features such as color, texture, and shape and the use of these features as query terms. Feature extraction is a crucial part of any such system. Current methods for feature extraction suffer from two main problems: firstly, many methods do not retain any spatial information, and secondly, the problem of invariance with respect to standard transformation is still unsolved. In this paper, we describe some results of a study on similarity evaluation in image retrieval using shape, texture, and color as content features. Images are retrieved based on similarity of features, where features of the query specification are compared with features of the image database to determine which images match similarly with given features. In this paper, we propose an effective method for image representation which utilizes fuzzy features. The text was submitted by the author in English. Ryszard S. Choraś is Professor of Computer Science in the Department of Telecommunications and EE of University of Technology and Agriculture, Bydgoszcz, Poland. He also holds a courtesy appointment with the Faculty of Mathematics, Technology, and Natural Sciences of Kazimierz Wielki University, Bydgoszcz and the College of Computer Science, Lódz, Poland. His research interests include image signal compression and coding, computer vision, and multimedia data transmission. He received his M.S. degree in Electrical Engineering from Electronics from the Technical University of Wroclaw, Poland in 1973, and his Ph.D. degree in Electronics from Technical University of Wroclaw, Poland, in 1980, and D.Sc. (Habilitation degree) in Computer Science from Warsaw Technical University, Poland, in 1993. Until 1973–1976 he was a member of the research staff at the Institute of Mathematical Machines Silesian Division, Gliwice, working on graphics hardware and human visual perception. In 1976, he joined University of Technology and Agriculture, Bydgoszcz, Poland, first as an Assistant, then as a Professor of Computer Science at the Department of Telecommunications and EE. From 1994 to 1996, he was also Professor of Computer Sciences of the Zielona Góra University, Poland. He has served as the Chairman of the Communication Switching Division and as Chief of the Image Processing and Recognition Group. Until 1996–2002 he was the Vice Rector of University of Technology and Agriculture, Bydgoszcz. Prof. Choraś has an expertise in EU Programs and National Programs, e.g., he was coordinator of EU Program CME-02060, EU Program on Continuous Education and Technology Transfer, and coordinator of national programs in IST and multimedia in e-learning. Prof. Choraś has authored two monographs, and over 130 book chapters, journal articles, and conference papers in the area of image processing. Professor Choraś is a member of the editorial boards of “Machine Vision and Graphics.” He is the editor-in-chief of “Image Processing and Communications Journal.” He has served on numerous conference committees, e.g., Visualization, Imaging, and Image Processing (VIIP), IASTED International Conference on Signal Processing, Pattern Recognition and Applications, International Conference on Computer Vision and Graphics, ICINCO International Conference on Informatics in Control, Automation and Robotics, ICETE International Conference on E-business and Telecommunication Networks, and CORES International Conference on Computer Recognition Systems, and many others. Prof Choraś is a member of the IASTED, WSEAS, various Committees of the Polish Academy of Sciences, TPO. When not working on academic ventures, Professor Choraś likes to relax with activities such as walking, tennis, and swimming.     

Understanding a bag of words by conceptual labeling with prior weights

World Wide Web - In many natural language processing tasks, e.g., text classification or information extraction, the weighted bag-of-words model is widely used to represent the semantics of text,...     

Fuzzy logic = computing with words

As its name suggests, computing with words (CW) is a methodology in which words are used in place of numbers for computing and reasoning. The point of this note is that fuzzy logic plays a pivotal role in CW and vice-versa. Thus, as an approximation, fuzzy logic may be equated to CW. There are two major imperatives for computing with words. First, computing with words is a necessity when the available information is too imprecise to justify the use of numbers, and second, when there is a tolerance for imprecision which can be exploited to achieve tractability, robustness, low solution cost, and better rapport with reality. Exploitation of the tolerance for imprecision is an issue of central importance in CW. In CW, a word is viewed as a label of a granule; that is, a fuzzy set of points drawn together by similarity, with the fuzzy set playing the role of a fuzzy constraint on a variable. The premises are assumed to be expressed as propositions in a natural language. In coming years, computing with words is likely to evolve into a basic methodology in its own right with wide-ranging ramifications on both basic and applied levels     

基于词袋模型的分布式拒绝服务攻击检测

针对分布式拒绝服务（DDoS）攻击有效荷载快速变化,人工干预需要依赖经验设定预警阈值以及异常流量特征码更新不及时等问题,提出一种基于二进制流量关键点词袋（BSP-BoW）模型的DDoS攻击检测算法。该算法可以自动从当前网络的流量数据中训练得到流量关键点（SP）,针对不同拓扑网络进行自适应异常检测,减少频繁更新特征集带来的人工成本。首先,对已有的攻击流量和正常流量进行均值聚类,寻找网络流量中的SP;然后,将原有的流量转化映射到相应SP上使用直方图进行形式化表达;最后,通过欧氏距离进行DDoS攻击的分类检测。在公开数据库DARPA LLDOS1.0上的实验结果表明,所提算法的异常网络流量识别率优于现有的局部加权学习（LWL）、支持向量机（SVM）、随机树（Random Tree）、logistic回归分析（logistic）、贝叶斯（NB）等方法。所提的基于词袋聚类模型算法在拒绝服务攻击的异常流量识别中有很好的识别效果和泛化能力,适合部署在中小企业（SME）网络流量设备上。     

文字书写过程描述字自动生成

文字书写过程描述字是文字书写自动教学系统教学知识点的核心字段。针对多语源导致知识点数据类型多、计算结构复杂及知识量大等特点,提出文字书写过程描述字自动生成方法。描述字由主导笔顺和约束笔顺组成;给出各类要素元编码空间关系;设计与实现描述字自动生成算法。汉字、英文、汉语拼音等文种文字融合实验及其分析表明,描述字结构能满足不同文种文字书写过程表达,生成方法能准确识别各种知识要素,与手工法相比不但提高了工作效率、知识的准确率,而且有效降低了信息冗余度。     

一种结合显著性检测与词袋模型的目标识别方法

针对词袋模型易受到无关的背景视觉噪音干扰的问题,提出了一种结合显著性检测与词袋模型的目标识别方法。首先,联合基于图论的视觉显著性算法与一种全分辨率视觉显著性算法,自适应地从原始图像中获取感兴趣区域。两种视觉显著性算法的联合可以提高获取的前景目标的完整性。然后,使用尺度不变特征变换描述子从感兴趣区域中提取特征向量,并通过密度峰值聚类算法对特征向量进行聚类,生成视觉字典直方图。最后,利用支持向量机对目标进行识别。在PASCAL VOC 2007和MSRC-21数据库上的实验结果表明,该方法相比同类方法可以有效地提高目标识别性能。     

复杂环境中基于视觉词袋模型的车辆再识别算法

根据公共安全部门在复杂环境中搜索出特定目标的迫切需求,将目标再识别（re-ID）技术应用到车辆识别领域,提出了一种基于视觉词袋（BoVW）模型的车辆再识别解决方案。首先,为解决复杂环境中遮挡、目标物位姿变化、目标物在图片中的大小位置存在差异等问题,提取出可基于不同尺度、不同位姿的改进基于部件的一对一局部特征（POOF）;其次,通过基于欧氏距离的聚类算法获取视觉词袋中的词汇集合;接着,将训练和测试集中的每张图像或目标转换为词袋中的词汇表述集;最后,利用基于改进保持直接简单原则的度量方法（KISSME）上的再排序方法分离出类间距离和类内距离,通过最近邻方法（KNN）获得识别结果。实验结果显示,在基础特征构建环节上该算法比冒泡银行算法（BB）识别率提升了3.85个百分点;其基于KISSME距离度量的改进再排序算法比贝叶斯再访问算法提升了3.14个百分点。最后,算法对目标位姿变化和局部遮挡具有的适应性和整体时效指标,进一步验证了其可适应于复杂环境应用的特色和优越性。     

适用于丢包信道的多描述图像量化方法

为提高不可靠网络条件下传输图像的重建质量,提出一种结合遗传算法和小波变换的多描述图像编码方法。利用离散小波变换(DWT)将源图像分解为4个重要性不同的子带,通过自适应遗传算法优化的量化步长对子带系数进行均匀量化,增大引入相关后各个图像描述中的低频信息比重。3组测试图像的仿真分析结果表明,在丢失2或3数据包时,相校基于固定步长的MDTC.DWT/UQ方法,该方法具有更好的鲁棒性,用假设检验的方法进一步验证了该方法的有效性。     

Fuzzy ADALINEs for gray image recognition

The paper aims at extending the scope of application of Widrow-Hoff 's ADALINE model from binary to gray-level (fuzzy) pattern recognition. The condition of stability for the extended ADALINE model has been derived and the algorithm for training the multilayered feedforward neural net consisting of ADALINE neurons have been presented. The time required for training the neural net is insignificantly small. Moreover, the scheme for the recognition of objects from their gray level images, using fuzzy ADALINE model, is translation, rotation and size invariant.     

超像素词包模型与SVM分类的图像标注

为了改善基于词包模型与支持向量机(SVM)分类一幅图对应一个标签的单标签分类问题,提出了一种基于超像素词包模型与SVM分类的图像标注算法.将超像素分割结果作为词包模型的基本单元,用词包模型生成的视觉词汇表示超像素区域特征,保留了图像中的同质区域,很好地利用了图像的区域特征.仿真结果表明,该方法能有效改善基于词包模型与SVM分类的单标签分类问题,且分类的准确性有所提高.     

Support vector description of clusters for content-based image annotation

Continual progress in the fields of computer vision and machine learning has provided opportunities to develop automatic tools for tagging images; this facilitates searching and retrieving. However, due to the complexity of real-world image systems, effective and efficient image annotation is still a challenging problem. In this paper, we present an annotation technique based on the use of image content and word correlations. Clusters of images with manually tagged words are used as training instances. Images within each cluster are modeled using a kernel method, in which the image vectors are mapped to a higher-dimensional space and the vectors identified as support vectors are used to describe the cluster. To measure the extent of the association between an image and a model described by support vectors, the distance from the image to the model is computed. A closer distance indicates a stronger association. Moreover, word-to-word correlations are also considered in the annotation framework. To tag an image, the system predicts the annotation words by using the distances from the image to the models and the word-to-word correlations in a unified probabilistic framework. Simulated experiments were conducted on three benchmark image data sets. The results demonstrate the performance of the proposed technique, and compare it to the performance of other recently reported techniques.