图像检索中融合语义和底层特征方法的研究

针对基于内容的图像检索系统检索效率不高的情况,通过在低层视觉特征上提取图像的颜色和纹理特征和空间信息,综合图像的语义特征,实现了对图像数据库的检索,最后,为了提高检索效率,把相关反馈技术引入到图像检索系统中。实验证明,该方法取得了较好的检索查全率和准确率。     

图像检索中融合语义和底层特征方法的研究

针对基于内容的图像检索系统检索效率不高的情况,通过在低层视觉特征上提取图像的颜色和纹理特征和空间信息,综合图像的语义特征,实现了对图像数据库的检索,最后,为了提高检索效率,把相关反馈技术引入到图像检索系统中。实验证明,该方法取得了较好的检索查全率和准确率。     

一种基于形状特征的颅骨图像检索方法

提出了一种基于内容的颅骨图像检索方法，介绍了基于内容的医学图像检索系统结构，阐述了图像预处理方法和特征参数提取过程。实验表明，该方法能检索出有相同病理特征的相似颅骨图像。     

一种融合语义的图像检索技术研究

针对基于内容的图像检索系统检索效率不高的情况,从目标图像外轮廓特征的提取、图像外轮廓特征的描述方式、图像轮廓的语义描述方式和相似度测定4个方面对基于内容的图像检索的研究状况进行了分析和研究。综合图像的外轮廓特征及轮廓的语义描述来实现对图像数据库的检索。实验结果表明,该方法提高了图像检索的精度和效度。     

一种融合语义的图像检索技术研究

针对基于内容的图像检索系统检索效率不高的情况,从目标图像外轮廓特征的提取、图像外轮廓特征的描述方式、图像轮廓的语义描述方式和相似度测定4个方面对基于内容的图像检索的研究状况进行了分析和研究.综合图像的外轮廓特征及轮廓的语义描述来实现对图像数据库的检索.实验结果表明,该方法提高了图像检索的精度和效度.     

基于内容的图像检索综述

本文简要介绍了基于内容的图像检索，给出了基于内容的图像检索系统的一般结构。对图像检索的发展进行了概述。对基于内容的图像检索的主要研究技术进行了详细和全面的论述，并介绍了几个典型的基于内容的图像检索系统。最后，指出了目前研究中存在的一些主要问题。     

科技资源图像检索的探讨

科技资源具有海量、复杂、异构、地理分布广的特点,为了高效查找和使用科技资源的图像,指出综合运用TBIR(基于文本的图像检索)和CBIR(基于内容的图像检索)共同检索科技资源图像,并列举了较为先进的图像检索系统,探讨了科技资源图像检索模型。     

基于t-SNE卷积编码的图像检索方法

为了提高基于内容图像检索系统的速度和精度,提出了一种基于t-SNE卷积编码的图像检索方法。该方法首先采用一个高精度卷积神经网络模型提取图像特征,然后通过定量分析模型不同层特征的检索性能,选择出最佳特征。其次将选择出的最佳特征使用t-SNE方法进行编码,降低特征维度的同时进一步减少图像特征中的噪声。最后,利用降维后的编码特征,实现基于内容的图像检索系统。实验结果表明：随着特征维度的降低,卷积编码方法不但不会降低检索精度,反而在某些情况下会提高检索精度。采用16维卷积编码特征,就可以超过传统方法128维编码特征的检索精度。而一旦特征维度降低8倍,可以使得特征的存储空间缩小8倍,图像检索效率大幅提高。因此,该方法可以有效提高基于内容图像检索系统的速度和精度。     

基于微生物菌种资源库的图像智能检索系统研究*

在建设微生物菌种资源平台的基础上, 设计开发微生物菌种资源数据库管理系统,实现基于内容的Web图像智能检索系统.该检索系统采用B/S体系架构,可根据微生物菌种文本信息或图像的形状、纹理和颜色特征进行检索,有效提高了微生物菌种图像信息搜索的查准率、查全率和查找速度.     

基于模糊颜色特征和模糊相似度的图像检索方法

基于内容的图像检索系统的性能主要依赖于两个关键技术:图像特征提取和图像特征匹配.文中提取了所有图像的颜色特征,并在颜色特征提取过程中采用了适当的模糊算法以得到图像的模糊颜色特征.图像特征匹配主要取决于两个图像特征向量之间的相似度,文中提出了一种新的模糊相似度衡量方法,该方法利用给定的查询图像与其k幅近邻图像之间的相似度构成查询图像的k维模糊特征向量,利用每幅被检索图像与查询图像的k幅近邻图像之间的相似度构成每幅被检索图像的k维模糊特征向量,计算查询图像的k维模糊特征向量与每幅被检索图像的k维模糊特征向量之间的模糊相似度,并将检索到的图像按照模糊相似度按从大到小的顺序反馈给用户.为了验证提出的模糊颜色特征的有效性,文中在WANG数据集上进行了一系列的实验对比;为了衡量基于不同相似度的图像检索系统的性能,文中在WANG,Corel-5k和Corel-10k数据集上分别进行了一系列的实验对比.实验结果表明,基于最大最小值的图像检索系统的性能优于基于其他3种常用相似度的图像检索系统的性能,而基于模糊相似度的图像检索系统的性能优于基于最大最小值的图像检索系统的性能.在WANG,Corel-5k和Corel-10k数据集上,基于模糊相似度的图像检索系统检索到的前20幅图像的平均查准率比基于最大最小值的图像检索系统检索到的前20幅图像的平均查准率分别高4.92％,17.11％和19.48％;基于模糊相似度的图像检索系统检索到的前100幅图像的平均查准率比基于最大最小值的图像检索系统检索到的前100幅图像的平均查准率分别高4.94％,22.61％和33.02％.     

Statistical correlation analysis in image retrieval

A statistical correlation model for image retrieval is proposed. This model captures the semantic relationships among images in a database from simple statistics of user-provided relevance feedback information. It is applied in the post-processing of image retrieval results such that more semantically related images are returned to the user. The algorithm is easy to implement and can be efficiently integrated into an image retrieval system to help improve the retrieval performance. Preliminary experimental results on a database of 100,000 images show that the proposed model could improve image retrieval performance for both content- and text-based queries.     

几种基于内容的图像检索的方法

基于内容的图像检索(CBIR)技术依赖于对图像特征(例如颜色直方图、纹理、草图、形状等)的提取,相对于传统的基于文本的图像检索方式,这种方式提高了检索效率和检索的准确率。文中主要介绍了基于颜色和基于纹理特征这两种特征提取方法。这两种方法既能够反映全局特征,又能够兼顾所感兴趣区域的局部特征,是基于内容的图像检索的两种非常有效的方法。     

基于LIRE和Struts的CBIR系统的设计与实现

随着图像数据的日益庞大．图像的检索显得越来越重要。在海量图像数据的环境下,基于内容的图像检索相比传统基于文本的图像检索更具优越性．就如何实现一个基于内容的图像检索系统进行详细的描述。以开源的Struts 2 Java Web框架,配合LIRE程序库,实现一个三层架构CBIRWeb应用程序。详细介绍实现流程,并附有主要代码。     

Image retrieval with a multi-modality ontology

Ontologies represent domain concepts and relations in a form of semantic network. Many research works use ontologies in the information matchmaking and retrieval. This trend is further accelerated by the convergence of various information sources supported by ontologies. In this paper, we propose a novel multi-modality ontology model that integrates both the low-level image features and the high-level text information to represent image contents for image retrieval. By embedding this ontology into an image retrieval system, we are able to realize intelligent image retrieval with high precision. Moreover, benefiting from the soft-coded ontology model, this system has good flexibility and can be easily extended to the larger domains. Currently, our experiment is conducted on the animal domain canine. An ontology has been built based on the low-level features and the domain knowledge of canine. A prototype retrieval system is set up to assess the performance. We compare our experiment results with traditional text-based image search engine and prove the advantages of our approach.     

DVD影片中基于内容的镜头查询技术与实现方法

基于内容的检索是广泛应用于多媒体系统中的一种不同于基于文本检索的方法,尤其是在图像与视频库中。当前随着DVD技术的发展,一张影碟中所包含的信息将越来越多,要查找其中用户需要的信息也变得越来越困难,尤其是对于故事情节较长的影片或容量较大的DVD影碟中所包含的几部影片,对它们进行基于内容的检索是非常必要的。该文提出一种针对DVD－VIDEO影碟的基于内容的检索方法,通过对视频镜头检测、代表帧提取以及相似镜头聚类等来形成一个影片场景浏览图,并将影片中每个镜头或场景间的前后转移关系用导航键联结,以完成对影片镜头的查找,使得用户对影片中相关镜头的检索与查找更加方便与快捷。     

基于改进Sobel算子的文物图像检索

基于内容的图像检索克服了基于文本的图像检索方法无法利用图像中听包含的丰富的视觉特征的问题,更符合人类认识事物的习惯,成为了近些年来图像检索的热点。其中,纹理特征是图像中一个重要的视觉特性。实验结合文物图像特点,首先将彩色文物图像转化为灰度图像,然后用Canny算子检测出图像边缘,再用改进的Sobel算子模拟共生矩阵检测的4个方向的纹理矩阵,最终将其转换为直方图,从而提取到文物图像的纹理特征。实验表明,使用这种纹理特征进行文物图像检索可以取得很好的检索效果。     

The study on content based multimedia data retrieval system

Of late, advance in hardware and communications technology has been rapidly increasing the demand for diverse multimedia information, which, including all image, audio, video, text, numerical data, etc., should be designed to excel the existing information processing system in the functions of data storage, search, transmission, display, etc. The newest image retrieval system is gradually being converted from text-based into content-based retrieval, which uses the image content itself as features. In content-based retrieval, how to combine the color, shape, layout, texture, etc. used for describing each image or object is considered an important element. The existing method has chiefly used histogram out of the content-based image method using color information, but this has a drawback in being sensitive to brightness of light and the object size in the image. Thus, the present methods is intended to design and implement a system that can retrieve images similar to the query image from image database without losing image information in the use of color features.     

Scalable search-based image annotation

With the popularity of digital cameras, more and more people have accumulated considerable digital images on their personal devices. As a result, there are increasing needs to effectively search these personal images. Automatic image annotation may serve the goal, for the annotated keywords could facilitate the search processes. Although many image annotation methods have been proposed in recent years, their effectiveness on arbitrary personal images is constrained by their limited scalability, i.e. limited lexicon of small-scale training set. To be scalable, we propose a search-based image annotation algorithm that is analogous to information retrieval. First, content-based image retrieval technology is used to retrieve a set of visually similar images from a large-scale Web image set. Second, a text-based keyword search technique is used to obtain a ranked list of candidate annotations for each retrieved image. Third, a fusion algorithm is used to combine the ranked lists into a final candidate annotation list. Finally, the candidate annotations are re-ranked using Random Walk with Restarts and only the top ones are reserved as the final annotations. The application of both efficient search techniques and Web-scale image set guarantees the scalability of the proposed algorithm. Moreover, we provide an annotation rejection scheme to point out the images that our annotation system cannot handle well. Experimental results on U. Washington dataset show not only the effectiveness and efficiency of the proposed algorithm but also the advantage of image retrieval using annotation results over that using visual features.     

A shape-based visual interface for text retrieval

We describe a shape-based visual interface for information retrieval and interactive exploration that exploits shape recognition. Our exploratory system uses procedurally generated shapes coupled with an underlying text-retrieval engine. A visual interface based on 3D shapes (glyphs) enhances traditional text-based queries and summarization. Our interface lets users visualize multidimensional relationships among documents and perceive more information than with conventional text-based interfaces. It promotes information overview and drill-down in support of analysis. Before describing our visual interface and application, we introduce information retrieval within the context of data mining and provide a brief overview of procedural shape generation. We then describe our current system and give a few relevant examples. Finally, we offer some ideas for future enhancements and direction