Search - shape indexing

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[Special Effects] ShapeIndexing DL : 0: Image indexing by Shape and Color.
Date : 2008-10-13 Size : 60.41kb User : Ian
[Special Effects] ShapeIndexing DL : 0: Image indexing by Shape and Color.
Date : 2025-07-03 Size : 60kb User : Ian
[Windows Develop] CBIR DL : 0: Content Based Image Retrieval
Date : 2025-07-03 Size : 4kb User : Ankush
[source in ebook] indexing66 DL : 0: The indexing of an image database is often referred as feature extraction. Mathematically, a feature is an n-dimensional vector, with its components computed by some image analysis. The most commonly used visual cues are color, texture, shape, spatial information, and motion in video. For example, a feature may represent the color information in an image
Date : 2025-07-03 Size : 1.38mb User : rach
[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
Date : 2025-07-03 Size : 392kb User : 周二牛
[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.
Date : 2025-07-03 Size : 2.59mb User : silkan_h