Image retrieval using multi-scale color clustering

Sehwan Kim, Woontack Woo, Yo-Sung Ho
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引用次数: 4

Abstract

A fundamental issue in content-based image retrieval is how to select image features that can represent image contents appropriately. A multi-scale color clustering algorithm based on human perceptual properties of color images is proposed for image retrieval. The multi-scale clustering algorithm is an unsupervised clustering method that utilizes the perceptual uniformity property in the (p,q) color space. The proposed color clustering algorithm produces a small set of representative color vectors for each image that capture color properties of the image, and a set of correlogram values that contain the spatial information of the image.
基于多尺度颜色聚类的图像检索
基于内容的图像检索中的一个基本问题是如何选择能够恰当地表示图像内容的图像特征。提出了一种基于人类对彩色图像感知特性的多尺度颜色聚类算法。多尺度聚类算法是一种利用(p,q)颜色空间感知均匀性的无监督聚类方法。提出的颜色聚类算法为每个图像生成一组具有代表性的颜色向量,用于捕获图像的颜色属性,并生成一组包含图像空间信息的相关图值。
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