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A Semi-local Method for Image Retrieval

机译:用于图像检索的半本地方法

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The visual content of an image is expressed by global or local features. Global features describe some properties of the image such as color, texture and shape. Local features were successfully used for object category recognition and classification to extract the local information from a set of interest points or regions. In this paper, we propose a semi-local method to extract the features based on the previous features extraction methods. Our technique is called the "Spatial Pyramid Matching: SPM". It works by partitioning the image into increasingly fine sub-regions (or blocs) and computing histograms of global features found inside each bloc. The results obtained by the proposed method are illustrated through some experiments on Wang and Holidays Dataset. The obtained Results show the simplicity and efficiency of our proposal.
机译:图像的视觉内容由全局或本地特征表示。全局特征描述了图像的一些属性,如颜色,纹理和形状。本地功能已成功用于对象类别识别和分类,以从一组感兴趣点或区域中提取本地信息。在本文中,我们提出了一种半本地方法,以基于先前的特征提取方法提取特征。我们的技术被称为“空间金字塔匹配:SPM”。它通过将图像划分为越来越精细的子区域(或集团)和计算每个BLOC中的全局特征的直方图。通过提出的方法获得的结果通过王和假期数据集的一些实验说明。获得的结果表明我们提案的简单性和效率。

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