Description: 基于内容的图像检索,基于CXIMAGE类的图像检索软件系统,能够实现文件夹下的相似图像检索,并且选择显示的图像.-content-based image retrieval, CXIMAGE category based image retrieval software system folders can be achieved under similar image retrieval, and the selection and display of images. Platform: |
Size: 275456 |
Author:suhuitao |
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Description: 一种基于HSV颜色空间的图像检索技术
随着大规模数字图像库的出现，基于内容的图像检索技术（CBIR）应运而生。而颜色特征是在图像检索中应用最为广泛的视觉特征，因此设计并实现一个基于颜色的图像检索系统具有一定的前沿性和实用性-an HSV color space-based image retrieval technology with the massive digital image library there. Content-Based Image Retrieval (CBIR) came into being. And color features in image retrieval application of the most extensive visual characteristics, Therefore the design and realization of a color-based image retrieval system with a front and practicality Platform: |
Size: 94208 |
Author:guoxiaoyong |
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Description: 基于内容的图像检索系统，本系统采用颜色特征进行检索。-Content-Based Image Retrieval System, the system uses the color feature retrieval. Platform: |
Size: 8949760 |
Author:吴月姝 |
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Description: 基于内容的图像检索系统
对两幅图像的特征匹配
CBIR系统
-Content-based image retrieval system to the two images of the feature matching CBIR system Platform: |
Size: 655360 |
Author:王新胜 |
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Description: 基于内容的图象检索系统代码图片预处理是为了使图片归一化，归一化的目的是将每幅原始图像调整到相同的尺寸和对应位置，从而消除平移、缩放和旋转对于图像鉴别的影响以方便对图片进行的特征提取。-Content-Based Image Retrieval System code image preprocessing is to enable the picture normalized, normalized objective is to adjust each piece of the original image to the same size and the corresponding location, thereby eliminating the translation, scaling and rotation for the image identification impact on the picture to facilitate feature extraction. Platform: |
Size: 1303552 |
Author:hu |
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Description: 经修改后的SIFT基于内容的图像检索系统
基于两副图的特征匹配-SIFT, as amended, content-based image retrieval system is based on the characteristics of Figure 2 matches Platform: |
Size: 4415488 |
Author:王新胜 |
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Description: The GIFT (the GNU Image-Finding Tool) is a Content Based Image Retrieval System (CBIRS: http://en.wikipedia.org/wiki/CBIR). It enables you to do Query By Example (QBE: http://en.wikipedia.org/wiki/QBE) on images, giving you the opportunity to improve query results by relevance feedback. For processing your queries the program relies entirely on the content of the images, freeing you from the need to annotate all images before querying the collection. Platform: |
Size: 793600 |
Author:yudaxia |
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Description: 该代码为用tamura算法提取图像的纹理特征，包括粗糙度、对比度等，可用于基于内容的图像检索系统-The code for the algorithm used tamura extracted texture features, including roughness, contrast, etc., can be used for content-based image retrieval system Platform: |
Size: 5120 |
Author:vivi |
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Description: Content-based medical image retrieval is now getting more and more attention in the
world, a feasible and efficient retrieving algorithm for clinical endoscopic images is urgently
required. Methods: Based on the study of single feature image retrieving techniques, including color
clustering, color texture and shape, a new retrieving method with multi-features fusion and relevance
feedback is proposed to retrieve the desired endoscopic images. Results: A prototype system is set
up to evaluate the proposed method’s performance and some evaluating parameters such as the
retrieval precision & recall, statistical average position of top 5 most similar image on various features, etc.
are therefore given. Conclusions: The algorithm with multi-features fusion and relevance feedback
gets more accurate and quicker retrieving capability than the one with single feature image retrieving
technique due to its flexible feature combination and interactive relevance feedback. Platform: |
Size: 359424 |
Author:gokul/goks |
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Description: Two novel contributions to Content Based Image Retrieval are presented and
discussed. The fi rst is a search engine for font recognition. The intended usage
is the search in very large font databases. The input to the search engine is an
image of a text line, and the output is the name of the font used when printing
the text. After pre-processing and segmentation of the input image, a local
approach is used, where features are calculated for individual characters. The
method is based on eigenimages calculated from edge fi ltered character images,
which enables compact feature vectors that can be computed rapidly. A system
for visualizing the entire font database is also proposed. Applying geometry
preserving linear- and non-linear manifold learning methods, the structure of
the high-dimensional feature space is mapped to a two-dimensional representa-
tion, which can be reorganized into a grid-based display. Platform: |
Size: 455680 |
Author:Chandana |
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Description: the demand for automatically recognizing and retrieving medical image for screening,reference and management is growing faster than ever.In this paper present an intelligent content based image retrieval system called I-Browsewhich integrate both iconic and semantic content for histopathological image
. Platform: |
Size: 747520 |
Author:jincy |
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Description: Image retri techniques are useful in many image-processing applications. Content-based image retri systems work with whole images and searching is based on comparison of the query. General techniques for image retri are color, texture and shape. These techniques are applied to get an image the image . They are not concerned with the various resolutions of the images, size and spatial color distribution. Hence all these methods are not appropriate to the art image retri . Moreover shape based retri s are useful only in the limited domain. The content and metadata based system gives images using an effective image retri technique. Many other image retri systems use global features like color, shape and texture. But the prior results say there are too many false positives while using those global features to search for similar images. Hence we give the new view of image retri system using both content and metadata -Image retri techniques are useful in many image-processing applications. Content-based image retri systems work with whole images and searching is based on comparison of the query. General techniques for image retri are color, texture and shape. These techniques are applied to get an image the image . They are not concerned with the various resolutions of the images, size and spatial color distribution. Hence all these methods are not appropriate to the art image retri . Moreover shape based retri s are useful only in the limited domain. The content and metadata based system gives images using an effective image retri technique. Many other image retri systems use global features like color, shape and texture. But the prior results say there are too many false positives while using those global features to search for similar images. Hence we give the new view of image retri system using both content and metadata Platform: |
Size: 39936 |
Author:kilo |
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Description: 基于内容的图像检索系统（Content Based Image Retri , 以下简称CBIR），是计算机视觉领域中关注大规模数字图像内容检索的研究分支。典型的CBIR系统，允许用户输入一张图像，在图像数据库（或本地机、或网络）中查找具有相同或相似内容的其它图片。本实训的基本功能要求是实现基于视觉特征的图像检索。-Content-based image retri system (Content Based Image Retri , hereinafter referred to as CBIR), is concerned about the large-scale computer vision field of digital image content retri research branch. Typical CBIR system that allows users to input an image, in the image to find (or local machine or network) with the same or similar content to other pictures. The basic functional requirements of the training is to realize image retri based on visual characteristics. Platform: |
Size: 10582016 |
Author:zrx |
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Search - Content Based Image Retrieval System