Content Based Image Retrieval by using Multi Layer Centroid Contour Distance

K. Arai, C. Rahmad
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引用次数: 10

Abstract

In this paper we present a new approach to measuring similarity between two shape of object. In conventional method, centroid contour distance (CCD) is formed by measuring distance between centroid (center) and boundary of object, but this method cannot capture if an object have multiple boundary in the same angle. We develop a novel approach feature shape by measuring distance between centroid (center) and boundary of object that can capture multiple boundaries in the same angle or multi-layer centroid contour distance (MLCCD). The experiment result on simulation dataset and plankton dataset show that the proposed method (MLCCD) better than the conventional method (CCD).
基于内容的多层质心轮廓距离图像检索
本文提出了一种测量物体形状相似性的新方法。在传统的方法中,质心轮廓距离(CCD)是通过测量物体的质心(中心)与边界之间的距离来形成的,但是如果一个物体有多个相同角度的边界,这种方法就无法捕获。本文提出了一种通过测量物体质心(中心)与物体边界之间的距离来获取物体形状特征的新方法,该方法可以捕获同一角度的多个边界或多层质心轮廓距离(MLCCD)。在模拟数据集和浮游生物数据集上的实验结果表明,该方法(MLCCD)优于传统方法(CCD)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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