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Convex Hull-Based Feature Selection in Application to Classification of Wireless Capsule Endoscopic Images

机译：基于凸壳的特征选择在无线胶囊内镜图像分类中的应用

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摘要

In this paper we propose and examine a Vector Supported Convex Hull method for feature subset selection. Within feature sub-spaces, the method checks locations of vectors belonging to one class with respect to the convex hull of vectors belonging to the other class. Based on such analysis a coefficient is proposed for evaluation of sub-space discrimination ability. The method allows for finding subspaces in which vectors of one class cluster and they are surrounded by vectors of the other class. The method is applied for selection of color and texture descriptors of capsule endoscope images. The study aims at finding a small set of descriptors for detection of pathological changes in the gastrointestinal tract. The results obtained by means of the Vector Supported Convex Hull are compared with results produced by a Support Vector Machine with the radial basis function kernel.
机译：在本文中，我们提出并研究了用于特征子集选择的向量支持的凸包方法。在特征子空间内，该方法检查属于一个类别的向量相对于属于另一类别的向量的凸包的位置。基于这种分析，提出了用于评估子空间辨别能力的系数。该方法允许找到子空间，在该子空间中一个类的向量聚类并且它们被另一类的向量包围。该方法适用于胶囊内窥镜图像的颜色和纹理描述符的选择。这项研究的目的是找到一小部分用于检测胃肠道病理变化的描述子。将通过矢量支持的凸包获得的结果与带有径向基函数内核的支持矢量机产生的结果进行比较。

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