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1.
通过抽取的特征进行图象检索的算法测试平台   总被引:11,自引:1,他引:10       下载免费PDF全文
基于内容的图象检索近年来得到了广泛的研究,人们已提出了许多基于特征的图象检索算法,但如何管理,比较、评价、组合应用这些检索算法已成为继续深入研究必须要解决的一个问题,为了解决此问题,建立了一个通过抽取的特征进行图象检索的算法实验平台,该平台既具有管理功能(包括管理各种算法、图象库和图片),又集成各种算法,以综合实现不同的检索功能(包括递进检索和综合检索),实验结果表明,借助平台对算法和图象进行集中管理,既可以方便地对各种基于特征的图象检索算法进行比较和评价,又有助于方便地形研究新的算法。  相似文献   

2.
Web上基于内容的图象检索集基于内容的图象检索和Internet网络这两项技术于一体,它对图象媒体的广泛应用具有一定的实用价值,同时对图象处理技术如何适应网络要求又有一定的理论研究价值,本文研究了特征提取、分布运算、网络实现等Web上基于内容的图象检索的相关技术,建立了一个单机上的图象检索系统并用该系统检验了自己提出的图象检索方法.另外在Web上实现了使用该方法的图象检索,实践证明小波形状不变矩对图象的形状匹配具有较好的效果.  相似文献   

3.
CBIR系统中的图象语义分割技术   总被引:3,自引:0,他引:3  
随着数字图象技术、宽带网络技术和数字存储设备技术的发展,在网络上存储、传输大规模分布式数字图象库成为可能,因此研究基于内容的图象检索技术成为近几年的热点。实现基于内容的图象检索系统的关键问题是实现图象的语义分割。该文分六类对现有的图象语义分割技术进行了全面的总结,为进一步研究基于内容的图象检索技术奠定了基础。  相似文献   

4.
多媒体系统中基于图象内容检索的特征指标设计   总被引:1,自引:0,他引:1  
在多媒体系统应用中基于图象内容检索的特征指标设计问题。对图象和视频的基于内容的检索方法晃成功地开发一个多媒体数据库系统的关键,而对图象泊特征指标的提取则是关键中的关键。目前,基于内容检索的多媒体系统应用还外于初始阶段,并于图象特征指标的设计还没有统一的框架。本文应用图形处理学科中提出的多种指标来作为关于图象检索的线索,并提出了一个初步的检索构思。  相似文献   

5.
基于内容的图象检索技术的研究和发展   总被引:14,自引:0,他引:14  
多媒体技术和数字图书馆的发展和应用,使基于图象内容的检索技术,成为图象处理和计算机视觉的前沿问题。图象数据库检索查询的研究目的就是实现自动地、智能化地检索和管理图象。文章详细介绍了该技术的研究状况和具体应用,并探讨了其发展前景。  相似文献   

6.
一种基于内容的图象检索方法的实现   总被引:7,自引:0,他引:7       下载免费PDF全文
现有的许多多媒体数据库系统只提供了基于媒体描述关键字的检索和查询,却忽略了另一个重要的信息来源——媒体的内容。基于内容的图象检索技术一般采用颜色直方图为特征,但是这种方法不能反映空间特性。本文在直方图技术的基础上引入了颜色对方法,将图象的空间特性反映出来,因而能检索具有清晰边界的图象,并且图象的大小变化和旋转以及轻微的光照变化不影响检索结果。实验结果表明这种方法改善了检索效果。  相似文献   

7.
利用小波和矩进行基于形状的图象检索   总被引:32,自引:2,他引:30       下载免费PDF全文
形状是图象中目标的重要特征,基于形状的图象检索近来在基于内容的图象库系统和管理和应用中得到越来越多的重视。现已研制的系统存在两个问题。一是性能的不稳定性;二是相对平移,旋转和尺度变换的变化性,针对上问题,该文提出了一种新的基于形状的图象检索算法。此算法先对亮度图象图象进行小波模极大值变换以得到多尺度的边界图象,再利用7个不变矩提取每一尺度边界图象的特征,所有尺度上的矩共同组成图象的特征向量。图象的  相似文献   

8.
图象检索算子开放测试平台T-Brief设计与实现   总被引:1,自引:0,他引:1  
基于内容的图象检索是近年来多媒体技术领域发展的一个热点之一,大量基于特征的检索算法不断涌现。该文介绍一个对算法开放的抽取特征检索图象的算法测试平台,该平台可以即时集成现有的多种不同算法,并便于管理,同时它还提供了诸如综合检索,渐进检索等功能,可用于算法研究,性能比较等。  相似文献   

9.
WWW上基于内容的图象检索系统   总被引:2,自引:0,他引:2  
基于内容的图象检索技术和网络技术的快速发展使得开发在线的图象检索系统成为可能。讨论WWW上基于内容图象检索系统的设计和实现要点,并详细介绍一个较为完整的WWW图象检索系统。  相似文献   

10.
基于图象内容的链码检索方法   总被引:4,自引:1,他引:3  
针对数字化图象检索问题,提出了一种基于图象内容的检索方法。这种检索方法是基于图象的灰度特征值,按照一定的分割方法,将图象区域化,并获取该区域的灰度特征描述值;然后把各个区域的灰度描述值按着规定的方向连接起来,生成一个用于检索图象的链码。实验结果表明这种基于图象内容的检索方法,较方便和准确地达到了图象检索之目的。  相似文献   

11.
Zhang  Hongjiang  Chen  Zheng  Li  Mingjing  Su  Zhong 《World Wide Web》2003,6(2):131-155
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.  相似文献   

12.
Pinto  Joey  Jain  Pooja  Kumar  Tapan 《Multimedia Tools and Applications》2021,80(11):16683-16709

Searching an image or a video in a huge volume of graphical data is a tedious time-consuming process. If this search is performed using the conventional element matching technique, the complexity of the search will render the system useless. To overcome this problem, the current paper proposes a Content-Based Image Retrieval (CBIR) and a Content-Based Video Retrieval (CBVR) technique using clustering algorithms based on neural networks. Neural networks have proved to be quite powerful for dimensionality reduction due to their parallel computations. Retrieval of images in a large database on the basis of the content of the query image has been proved fast and efficient through practical results. Two images of the same object, but taken from different camera angles or have rotational and scaling transforms is also matched effectively. In medical domain, CBIR has proved to be a boon to the doctors. The tumor, cancer etc can be easily deducted comparing the images with normal to the images with diseases. Java and Weka have been used for implementation. The thumbnails extracted from the video facilitates the video search in a large videos database. The unsupervised nature of Self Organizing Maps (SOM) has made the software all the more robust.

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13.
基于内容的图象检索系统的设计与实现   总被引:2,自引:0,他引:2       下载免费PDF全文
依据当前对图象查询的要求,本文设计了一套完整的基于内容的图象信息检索系统,该系统较以往的各种系统,功能更加全面。对基于内容的图象信息检索算法作了研究.重点阐述了对颜色、边缘、纹理等全局特征的提取与匹配算法。实验结果表明,该系统能有效、快速地检索大规模的图象数据库,具有一定的应用价值。  相似文献   

14.
视频数据中游动字幕的检测是现代智能监播系统中的一个重要问题,同时也是后续诸多视频数据处理的一个基本前提和出发点。将基于内容的数据检索技术应用于视频游动字幕的检测算法,设计了游动字幕矢量化方法,给出了相似度的准则,提出了游动字幕的检测算法,并给出了相应的数值实验及算法的复杂度分析。实验证明结果是正确和有效的。  相似文献   

15.

In the recent years the rapid growth of multimedia content makes the image retrieval a challenging research task. Content Based Image Retrieval (CBIR) is a technique which uses features of image to search user required image from large image dataset according to the user’s request in the form of query image. Effective feature representation and similarity measures are very crucial to the retrieval performance of CBIR. The key challenge has been attributed to the well known semantic gap issue. The machine learning has been actively investigated as possible solution to bridge the semantic gap. The recent success of deep learning inspires as a hope for bridging the semantic gap in CBIR. In this paper, we investigate deep learning approach used for CBIR tasks under varied settings from our empirical studies; we find some encouraging conclusions and insights for future research.

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16.

Content based image retrieval (CBIR) systems provide potential solution of retrieving semantically similar images from large image repositories against any query image. The research community are competing for more effective ways of content based image retrieval, so they can be used in serving time critical applications in scientific and industrial domains. In this paper a Neural Network based architecture for content based image retrieval is presented. To enhance the capabilities of proposed work, an efficient feature extraction method is presented which is based on the concept of in-depth texture analysis. For this wavelet packets and Eigen values of Gabor filters are used for image representation purposes. To ensure semantically correct image retrieval, a partial supervised learning scheme is introduced which is based on K-nearest neighbors of a query image, and ensures the retrieval of images in a robust way. To elaborate the effectiveness of the presented work, the proposed method is compared with several existing CBIR systems, and it is proved that the proposed method has performed better then all of the comparative systems.

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17.
With the evolution of digital technology, there has been a significant increase in the number of images stored in electronic format. These range from personal collections to medical and scientific images that are currently collected in large databases. Many users and organizations now can acquire large numbers of images and it has been very important to retrieve relevant multimedia resources and to effectively locate matching images in the large databases. In this context, content-based image retrieval systems (CBIR) have become very popular for browsing, searching and retrieving images from a large database of digital images with minimum human intervention. The research community are competing for more efficient and effective methods as CBIR systems may be heavily employed in serving time critical applications in scientific and medical domains. This paper proposes an extremely fast CBIR system which uses Multiple Support Vector Machines Ensemble. We have used Daubechies wavelet transformation for extracting the feature vectors of images. The reported test results are very promising. Using data mining techniques not only improved the efficiency of the CBIR systems, but they also improved the accuracy of the overall process.  相似文献   

18.
交互式遗传算法在基于内容的图像检索中的应用   总被引:6,自引:1,他引:6  
基于内容的图像检索方法是根据图像所包含的色彩、纹理、形状以及对象的空间关系等信息,通过建立图像的特征矢量,并将其作为图像的索引来进行图像检索的技术,其检索效果与图像特征矢量的编码方式以及具体的图像检索方法都有着很密切的关系。为了提高图像的检索效率,提出了一种基于交互式遗传算法的图像检索方法,该方法首先采用“变均分单元”法对图像进行分割,并对图像的特征信息加以汇总,形成图像的特征矢量;然后在此基础上,使用“螺旋式”的图像拆分方式通过对图像特征数据进行编码来生成图像染色体,并使它参与遗传算法中的各种遗传操作。在图像的检索过程中,该方法采用交互式遗传算法,首先对系统在每一步提供的候选图像集进行评价,然后利用非均匀遗传算子来从图像库中选出接近用户需求的图像。进一步的实验肯定了其在基于内容的图像检索过程中的有效性,与其他相关工作的比较结果说明,该方法具有简捷、高效的特点。  相似文献   

19.
基于内容的图象检索是图象理解应用于多媒体领域的产物,是下一代智能多媒体数据库的关键技术。本文针对基于内容的静态图象检索,提出了一种度量图象间相似程度的方法,同时还给出了一个通过分层聚类构造二叉树式分层索引数据结构的算法。  相似文献   

20.
基于图像中心加权特征的图像检索   总被引:10,自引:0,他引:10  
本文讨论了基于内容的图像检索系统中特征提取的技术,并提出了一种基于图像中心加权特征提取算法,即对图像不同位置提取的特征采用不同的加权系数,越靠近图像中心加权系数越大。最后使用支持向量机技术作为图像分类器进行图像的检索。实验表明该系统能有效地检索大规模的图像数据厍,并取得了比较好的效果。  相似文献   

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