Search - CBIR shape

Search - CBIR shape - List

[Software Engineering] ShapeFeatrueCBIR DL : 0: 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.
Update : 2025-02-17 Size : 61kb Publisher : 文演
[Windows Develop] CBIR DL : 0: Content Based Image Retrieval
Update : 2025-02-17 Size : 4kb Publisher : Ankush
[Graph program] 1 DL : 0: 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
Update : 2025-02-17 Size : 100kb Publisher : 倪晓雷
[Other] shape DL : 0: CBIR with Shape method, very good
Update : 2025-02-17 Size : 703kb Publisher : Mahesh
[Other] Key_to_Computer_Network_4th_Edition DL : 0: 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
Update : 2025-02-17 Size : 253kb Publisher : 田七
[Special Effects] NSCT DL : 0: 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
Update : 2025-02-17 Size : 392kb Publisher : 周二牛
[matlab] FEATURES DL : 0: 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)
Update : 2025-02-17 Size : 1kb Publisher : dr.n.a.s
[Special Effects] CBIR-FOR-ENDOSCOPIC-IMAGES DL : 0: 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.
Update : 2025-02-17 Size : 351kb Publisher : gokul/goks
[Program doc] CBIR-document DL : 0: 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.
Update : 2025-02-17 Size : 297kb Publisher : keerthi
[Special Effects] CBIR DL : 0: 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.
Update : 2025-02-17 Size : 3.36mb Publisher : jack
[Special Effects] fuliye DL : 0: 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.
Update : 2025-02-17 Size : 341kb Publisher : 劳世华
[Other] Effective-image-retrieval-using-shape-descriptor. DL : 0: about cbir with shape features
Update : 2025-02-17 Size : 38kb Publisher : venisha
[assembly language] zhifangtujiansuo DL : 0: 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.
Update : 2025-02-17 Size : 2kb Publisher : 周佳森
[Documents] CBIR DL : 0: 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
Update : 2025-02-17 Size : 5.26mb Publisher : liuyi
[Special Effects] CBIR DL : 0: 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
Update : 2025-02-17 Size : 341kb Publisher : 马亚琼
[Other] The-X-ray-Chest-Image-Retrieval-Based-on-Feature- DL : 0: 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
Update : 2025-02-17 Size : 879kb Publisher : Salkoum
[Other] ImageRetrieval-master DL : 0: CBIR using texture and shape feature
Update : 2025-02-17 Size : 3.29mb Publisher : cahya
[Other] FINAL_CBIR_1187556 DL : 0: CBIR project using shape moment color
Update : 2025-02-17 Size : 5.27mb Publisher : minata
[Program doc] 0alaya-cheikh2004 DL : 0: 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.
Update : 2025-02-17 Size : 2.59mb Publisher : silkan_h
[Special Effects] CBIR DL : 0: 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.)
Update : 2025-02-17 Size : 64kb Publisher : 大王123456