融合傅里叶描述子和尺度不变特征转换特征的商标检索

针对传统商标检索算法中全局特征容易造成误检,而局部特征SIFT对轮廓描述能力不强及算法复杂度高的问题,提出了一种融合图像全局特征和局部特征的商标检索算法。其中全局特征反应了图像的整体信息,这些信息可用来较快地建立候选图像库,而局部特征则可以更准确地与候选图像进行匹配。首先提取图像的傅里叶描述子进行初步检索,并按相似度排序,然后在此结果集的基础上对候选图像通过提取SIFT特征进行精确匹配。实验结果表明,该方法既保持了SIFT特征较高的查全率和查准率,优于傅里叶描述子单一特征,而且检索速度比SIFT单一特征显著提高,能很好地应用于商标图像检索系统中。     

一种改进的基于SIFT特征的商标检索方法

利用商标图像的形状特征,提出了一种融合图像全局特征和局部特征的商标检索算法。其中全局特征反映了图像的整体信息,这些信息可用来较快地建立候选图像库,而局部特征则可以更准确地与候选图像进行匹配。提取图像的傅里叶描述子进行初步检索,按相似度排序,在此结果集的基础上对候选图像通过提取SIFT特征进行精确匹配。实验结果表明,该方法既保持了SIFT特征的良好描述能力,又减少了精确匹配需要的计算次数,降低了复杂度。     

融合HU不变矩和SIFT特征的商标检索

利用商标图像的形状特征,提出了一种融合图像全局特征和局部特征的商标检索算法。其中全局特征反应了图像的整体信息,这些信息可用来较快地建立候选图像库,而局部特征则可以更准确地与候选图像进行匹配。提取图像的HU不变矩进行初步检索,按相似度排序,在此结果集的基础上对候选图像通过提取SIFT特征进行精确匹配。实验结果表明,该方法既保持了SIFT特征的良好描述能力,又减少了精确匹配需要的计算次数,降低了复杂度。     

基于平均检索精度的图像特征融合方法

在基于内容的图像检索中,不同图像特征反映了图像不同侧面的内在特性,如何有效地组织和利用这些特征从而提高系统的检索性能是一个值得研究的课题．首先提出了特征互补率的定义,通过计算互补矩阵有指导地选择融合特征集．实验结果表明,互补矩阵能够很好地估计特征之间的补充能力．同时提出了基于平均检索精度的特征线性融合方法,并在一个包含12000张异质图像的大型图像库上与当前图像检索中最常用的几种方法进行了对比实验,结果表明这种方法具有更高的精度．     

基于特征的二值商标分层检索方法

针对以往商标图像搜索方法单一,并且缺少用户反馈的缺点,文中提出一种基于形状特征和纹理特征的二值商标图像分层搜索方法.该方法首先利用HU不变矩提取图像的形状特征;然后利用欧氏距离来测量图像的相似度,进行第一次筛选得到一个数目不定的候选集;接着利用灰度直方图的方法提取候选集中的图像的纹理信息,进行第二次筛选;最后通过用户的反馈进行不断的优化.从而使检索出来的图像更加迅速和准确,更加符合人的视觉感受.     

基于NMI不变特征的二值商标图像检索方法研究

商标图像的检索在图像库系统管理和应用中得到了越来越多的重视。文中针对二值商标图像，提出一种基于 NMI 特征的商标图像检索方法。其特点是计算简单，精确度高，具有抗几何畸变性，对滤波、平滑、压缩等图像操作有一定的鲁棒性．试 验结果表明，该方法具有一定实用价值，可用于二值商标图像检索。     

基于文本与语义相关性分析的图像检索

为了更加有效地检索到符合用户复杂语义需求的图像，提出一种基于文本描述与语义相关性分析的图像检索算法。该方法将图像检索分为两步：基于文本语义相关性分析的图像检索和基于SIFT特征的相似图像扩展检索。根据自然语言处理技术分析得到用户文本需求中的关键词及其语义关联，在选定图像库中通过语义相关性分析得到“种子”图像；接下来在图像扩展检索中，采用基于SIFT特征的相似图像检索，利用之前得到的“种子”图像作为查询条件，在网络图像库中进行扩展检索，并在结果集上根据两次检索的图像相似度进行排序输出，最终得到更加丰富有效的图像检索结果。为了证明算法的有效性，在标准数据集Corel5K和网络数据集Deriantart8K上完成了多组实验，实验结果证明该方法能够得到较为精确地符合用户语义要求的图像检索结果，并且通过扩展算法可以得到更加丰富的检索结果。     

基于几何流形能量最小化的图像检索算法

提出了一种基于几何流形能量GEOMEN(geometric manifold energy)最小化的图像检索算法。许多基于流形的检索算法都是在图像的特征空间提取相应的语义流形空间,进而在语义空间中进行图像检索排序。将图像的检索看作一个图像数据库中搜索一个最优图像能量环的问题。图像能量环表示了图像之间的联系和相关性,通过最小化GEOMEN可以得到最优图像环。最小化的求解涉及到一个组合优化的问题,传统的禁忌搜索算法在选择最优候选集时非常耗时,提出一种智能的积极禁忌搜索算法求解最优环,实验表明提出的算法检索性能高,可以得到较高的查全率与准确率。     

基于内容图像检索中的特征性能评价

在基于内容的图像检索中，不同图像特征反映了图像各个侧面的内在特性，因此，在使用图像特征进行检索时存在多种相似性度量方法．特征以及特征间相似性度量方法的选取是当前CBIR研究的一个重要课题．评估了CBIR系统中使用的图像特征在不同相似性度量方法下及多种特征在不同图像库上的检索性能，为CBIR系统的设计和实现提供一定的依据．通过实验发现，图像特征的检索性能不仅同相似性度量方法有关系，同时与图像库也有密切的关系．     

基于融合多特征的社会网上图像检索方法

由于用户标注的非专业性及随意性,社会图像网站在图像检索过程中,其检索结果一般不够理想,针对这一现状,提出融合多特征的图像检索方法。依据用户输入的初始检索词,由系统提供给用户候选图像集,依据用户选择的目标图像,结合候选集中图像底层特征、社群主题、标签顺序等多属性构建出某一候选图像的多特征向量,依据计算不同向量与目标图像之间的距离,提交给用户距离最近的图像集,完成图像检索。仿真实验依据Flickr网站上真实数据展开,试验结果表明本方法的有效性。     

Trademark image retrieval using synthetic features for describing global shape and interior structure

A trademark image retrieval (TIR) system is proposed in this work to deal with the vast number of trademark images in the trademark registration system. The proposed approach commences with the extraction of edges using the Canny edge detector, performs a shape normalisation procedure, and then extracts the global and local features. The global features capture the gross essence of the shapes while the local features describe the interior details of the trademarks. A two-component feature matching strategy is used to measure the similarity between the query and database images. The performance of the proposed algorithm is compared against four other algorithms.     

基于受限随机选择和检索结果集的相关反馈

本文将相关反馈看成一个二分类问题,从反馈中固有的问题和用户的需求为出发点,在传统的移动查询点的基础上,提出了改进的基于受限随机选择和检索结果集的图像检索相关反馈系统。实验证明,该方法很好的解决了反馈中固有的小样本问题和正负样本不对称的问题。同时,综合利用用户的反馈信息,在较少的反馈次数内得到了较好的检索准确率。     

Discovering Text Databases with Neural Nets

Since documents on the Web are naturally partitioned into many text databases, the efficient document retrieval process requires identifying the text databases that are most likely to provide relevant documents to the query and then searching for the identified text databases. In this paper, we propose a neural net based approach to such an efficient document retrieval. First, we present a neural net agent that learns about underlying text databases from the user's relevance feedback. For a given query, the neural net agent, which is sufficiently trained on the basis of the BPN learning mechanism, discovers the text databases associated with the relevant documents and retrieves those documents effectively. In order to scale our approach with the large number of text databases, we also propose the hierarchical organization of neural net agents which reduces the total training cost at the acceptable level. Finally, we evaluate the performance of our approach by comparing it to those of the conventional well-known approaches. Received 5 March 1999 / Revised 7 March 2000 / Accepted in revised form 2 November 2000     

Content-based image retrieval by using tree-structured features and multi-layer self-organizing map

A new approach for content-based image retrieval (CBIR) is described. In this study, a tree-structured image representation together with a multi-layer self-organizing map (MLSOM) is proposed for efficient image retrieval. In the proposed tree-structured image representation, a root node contains the global features, while child nodes contain the local region-based features. This approach hierarchically integrates more information of image contents to achieve better retrieval accuracy compared with global and region features individually. MLSOM in the proposed method provides effective compression and organization of tree-structured image data. This enables the retrieval system to operate at a much faster rate than that of directly comparing query images with all images in databases. The proposed method also adopts a relevance feedback scheme to improve the retrieval accuracy by a respectable level. Our obtained results indicate that the proposed image retrieval system is robust against different types of image alterations. Comparative results corroborate that the proposed CBIR system is promising in terms of accuracy, speed and robustness.     

Relevance Feedback and Learning in Content-Based Image Search

A major bottleneck in content-based image retrieval (CBIR) systems or search engines is the large gap between low-level image features used to index images and high-level semantic contents of images. One solution to this bottleneck is to apply relevance feedback to refine the query or similarity measures in image search process. In this paper, we first address the key issues involved in relevance feedback of CBIR systems and present a brief overview of a set of commonly used relevance feedback algorithms. Almost all of the previously proposed methods fall well into such framework. We present a framework of relevance feedback and semantic learning in CBIR. In this framework, low-level features and keyword annotations are integrated in image retrieval and in feedback processes to improve the retrieval performance. We have also extended framework to a content-based web image search engine in which hosting web pages are used to collect relevant annotations for images and users' feedback logs are used to refine annotations. A prototype system has developed to evaluate our proposed schemes, and our experimental results indicated that our approach outperforms traditional CBIR system and relevance feedback approaches.     

<Emphasis Type="Italic">I-Quest</Emphasis>: an <Emphasis Type="Italic">i</Emphasis>ntelligent <Emphasis Type="Italic">que</Emphasis>ry <Emphasis Type="Italic">st</Emphasis>ructuring based on user browsing feedback for semantic retrieval of video data

In spite of significant improvements in video data retrieval, a system has not yet been developed that can adequately respond to a user’s query. Typically, the user has to refine the query many times and view query results until eventually the expected videos are retrieved from the database. The complexity of video data and questionable query structuring by the user aggravates the retrieval process. Most previous research in this area has focused on retrieval based on low-level features. Managing imprecise queries using semantic (high-level) content is no easier than queries based on low-level features due to the absence of a proper continuous distance function. We provide a method to help users search for clips and videos of interest in video databases. The video clips are classified as interesting and uninteresting based on user browsing. The attribute values of clips are classified by commonality, presence, and frequency within each of the two groups to be used in computing the relevance of each clip to the user’s query. In this paper, we provide an intelligent query structuring system, called I-Quest, to rank clips based on user browsing feedback, where a template generation from the set of interesting and uninteresting sets is impossible or yields poor results.

Integrated probability function and its application to content-based image retrieval by relevance feedback

In the last few years, we have seen an upsurge of interest in content-based image retrieval (CBIR)—the selection of images from a collection via features extracted from images themselves. Often, a single image attribute may not have enough discriminative information for successful retrieval. On the other hand when multiple features are used, it is hard to determine the suitable weighing factors for various features for optimal retrieval. In this paper, we present a relevance feedback framework with Integrated Probability Function (IPF) which combines multiple features for optimal retrieval. The IPF is based on a new posterior probability estimator and a novel weight updating approach. We perform experiments on 1400 monochromatic trademark images have been performed. The proposed IPF is shown to be more effective and efficient to retrieve deformed trademark images than the commonly used integrated dissimilarity function. The new posterior probability estimator is shown to be generally better than the existing one. The proposed novel weight updating approach by relevance feedback is shown to be better than both the existing scoring approach and the existing ratio approach. In experiments, 95% of the targets are ranked at the top five positions. By two iterations of relevance feedback, retrieval performance can be improved from 75% to over 95%. The IPF and its relevance feedback framework proposed in this paper can be effectively and efficiently used in content-based image retrieval.