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Description: 概括论述了形状检索的方法，包括区域特征和轮廓特征。-Outlined the shape retrieval methods, including the regional characteristics and profile characteristics.
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Author: 文演 |
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Description: Content Based Image Retrieval
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Size: 4096 |
Author: Ankush |
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Description: 状是含有高层语义信息的视觉特征，在基于内容的图像检索及图像识别中具有重要的应用价值。有很多种描述子可以描述图像的形状特征，傅立叶描述子可以把二维的图像轮廓信息简化成一维问题进行处理，应用非常广泛。然而自然图像的形状特征通常是杂乱的，有噪声的，提出了一种图像预处理方法，得到净化的形状图像，通过实验研究傅立叶描述子算法提取形状特征的效果。-Abstract Shape is a visual feature which contains intrinsic high-level semantics, and has a great application value in CBIR(Content-Based Image Retrieval) and IR(Image Recognition). There are many descriptors for shape feature. Fourier descriptor predigests 2-demensional image information to 1-demensional signal and be used widely. In fact, the shape of natural image is often messy and noisy. So, this paper proposes a preprocessing method which can clean the noisy shape image, and then researches and analyses the shape feature extraction with Fourier descriptor method with an experiment.
Keywords Shape, Fourier Descriptor, Feature Extraction, CBIR(Content-Based Image R
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Author: 倪晓雷 |
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Description: CBIR with Shape method, very good
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Author: Mahesh |
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Description: 这是ANDREW S. TANENBAUM编著的计算机网络教材的参考英文版答案，对使用本教材的人帮助十分大-ANDREW S. TANENBAUM This is a computer network edited English version of answers to reference materials on the use of the materials people to help very large
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Author: 田七 |
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Description: 1．分析研究了基于内容的图像检索系统的工作原理，关键技术如：纹
理、形状等图像底层特征的描述方法， 图像间的相似性度量方法，
图像库索引机制等。
2．研究了基于纹理特征的图像检索方法，并提出了一种基于NSCT 变
换的纹理特征提取方法。通过对SAR 图像及相关图像进行NSCT
分解，计算不同尺度不同方向上的系数幅度序列的均值，标准方差
和三阶中心矩，以此构成特征向量来描述图像的纹理。实验证明本
文提出的采用NSCT 算法有较好的特征提取效果，引入三阶中心矩
作为特征向量优于只使用均值和方差的组合特征，提高了图像检索
的查准率。
3．研究了基于形状特征的图像检索方法，并提出一种基于NSCT变换
的形状特征提取方法。把改进型Canny算子和NSCT变换相结合，先
对SAR图像及相关图像运用改进型Canny算子提取边缘，在此基础
上再进行NSCT变换，把图像的形状信息分解到不同尺度不同方向
上，从而保留各个频率分量，减少了图像形状信息的丢失。-1. First we analyze and study the principle of image retrieval system and
key techniques and algorithms of CBIR, such as the low-level feature
descriptions including texture, shape, the similarity measure between
images, the indexing methods and so on
2. Researching on the texture-based image retrieval algorithm, we
propose an algorithm of texture feature extraction based on the
Nonsubsampled Contourlet transform in this thesis. The image is
decomposed by the Nonsubsampled Contourlet transform. The mean,
standard deviation and third central moment of the magnitude of the
Nonsubsampled Contourlet coefficients at different scales and directions
are computed to extract the texture feature vector.Experiment proves the
third central moment added in NSCT arithmetic is overperformded than
only use the mean and standard deviation, and precision ratio has
improved.
3. Researching on the shape-based image retrieval algorithm, we propose
an algorithm of shape feature extraction bas
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Author: 周二牛 |
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Description: 3 files: imageslicer for splitting RGB image to 8 bit plans, getTopology to extract topology features features, and getShape to extract shape features . (good features for CBIR systems)
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Size: 1024 |
Author: dr.n.a.s |
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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.
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Author: gokul/goks |
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Description: CBIR is retrieval of images based on some query or example images. It is also called Query based image retrieval. Firstly, this report outlines a description of the primitive features of an image color and shape. These features are extracted and used as the basis for a similarity check between images. The final result is a MatLab built software application, with an image database, that utilized color and shape features of the images in the database as the basis of comparison and retrieval.
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Author: keerthi |
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Description: 本源码是用C++编写的基于内容的图像检索，采用形状、颜色、纹理三个特征的加权值作为特征向量。-The source code is written in c++ of content-based image retrieval, the shape, color, texture three characteristics of the weighted value as the characteristic vector.
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Author: jack |
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Description: 傅立叶描述子是分析和识别物体形状的重要方法之一．利用基于曲线多边形近似的连续傅立叶变换方法 计算傅立叶描述子，并通过形状的主方向消除边界起始点相位影响的方法，定义了新的具有旋转、平移和尺度不变 性的归一化傅立叶描述子．与使用离散傅立叶变换和模归一化的传统傅立叶描述子相比，新的归一化傅立叶描述 子同时保留了模与相位特性，因此能够更好地识别物体的形状．实验表明这种新的归一化傅立叶描述子比传统的 傅立叶描述子能够更加高效、准确地识别物体的形状．-Abstract Shape is a visual feature which contains intrinsic high-level semantics, and has a great application value in CBIR(Content-Based Image Retrieval) and IR(Image Recognition). There are many descriptors for shape feature. Fourier descriptor predigests 2-demensional image information to 1-demensional signal and be used widely. In fact, the shape of natural image is often messy and noisy. So, this paper proposes a preprocessing method which can clean the noisy shape image, and then researches and analyses the shape feature extraction with Fourier descriptor method with an experiment.
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Size: 349184 |
Author: 劳世华 |
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Description: about cbir with shape features
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Author: venisha |
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Description: 基于内容的图像检索（Content-based Image Retrieval，简称CBIR）技术被提出。这一技术的出现提高了图像检索的准确性，它通过提取图像本身的内在客观特征如颜色、纹理、形状、布局等关系，并比较这些视觉特征间的相似性，自动搜索出符合用户要求的图像。-Content-based image retrieval (Content-based Image Retrieval, referred to as CBIR) techniques have been proposed. The emergence of this technology to improve the accuracy of the image retrieval, intrinsic objective characteristics by extracting an image itself such as color, texture, shape, layout relations, and compare the similarity between the visual characteristics, automatically searched out in accordance with the user requirements images.
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Size: 2048 |
Author: 周佳森 |
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Description: 基于内容的图像检索 基于颜色、纹理、形状的图像检索
基于区域的图像检索
基于语义的图像检索
相关反馈
-Based on the content-based image retrieval based on color, texture, shape-based image retrieval region-based image retrieval based on semantic image retrieval relevance feedback
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Size: 5519360 |
Author: liuyi |
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Description: 综合多个特征的图像检索，包括颜色、形状和纹理等-Integrated multiple features for image retrieval, including color, shape and texture
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Author: 马亚琼 |
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Description: Based on the analysis of methods of CBIR and
chest image characteristic, in this paper, color
correlogam, dominant color of partition, gray level
co-occurrence matrix, gray-gradient co-occurrence
matrix and shape invariant moments were extracted as
retrieval feature. After comparison of their retrievals,
feature fusion and relevance feedback is proposed.
Experiments proved that the combining color, texture
with shape feature gets effective retrieval and relevance
feedback further more improves retrie
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Size: 900096 |
Author: Salkoum |
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Description: CBIR using texture and shape feature
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Author: cahya |
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Description: CBIR project using shape moment color
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Author: minata |
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Description: Query by content, or content-based retri has recently been proposed as
an alternative to text-based retri for media such as images, video and audio.
Text-based retri is no longer appropriate for indexing such media, for several
reasons. Firstly, keyword annotation is labor intensive, and it is not even possible
when large sets of images are to be indexed. Secondly, these annotations are
drawn a predefined set of keywords which cannot cover all possible concepts
images may represent. Finally, keywords assignment is subjective to the person
making it. Therefore, content-based image retri (CBIR) systems propose to
index the media documents based on features extracted their content rather
than by textual annotations. For still images, these features can be color, shape,
texture, objects layout, edge direction, etc.-Query by content, or content-based retri has recently been proposed as
an alternative to text-based retri for media such as images, video and audio.
Text-based retri is no longer appropriate for indexing such media, for several
reasons. Firstly, keyword annotation is labor intensive, and it is not even possible
when large sets of images are to be indexed. Secondly, these annotations are
drawn a predefined set of keywords which cannot cover all possible concepts
images may represent. Finally, keywords assignment is subjective to the person
making it. Therefore, content-based image retri (CBIR) systems propose to
index the media documents based on features extracted their content rather
than by textual annotations. For still images, these features can be color, shape,
texture, objects layout, edge direction, etc.
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Author: silkan_h |
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Description: 基于内容的图像检索，能搜索相似图像。基于颜色，形状和纹理的图像检索。(Content based image retrieval can search similar images. Image retrieval based on color, shape and texture.)
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Size: 65536 |
Author: 大王123456 |
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