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Description: Image indexing by Shape and Color.
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Size: 61861 |
Author: Ian |
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Description: Image indexing by Shape and Color.
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Size: 61440 |
Author: Ian |
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Description: Content Based Image Retrieval
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Size: 4096 |
Author: Ankush |
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Description: 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
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Size: 1445888 |
Author: rach |
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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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Size: 401408 |
Author: 周二牛 |
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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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Size: 2715648 |
Author: silkan_h |
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