A Conic Section Classifier and its Application to Image Datasets.
A Conic Section Classifier and its Application to Image Datasets.
复制标题
圆锥曲线分类器及其在图像数据集上的应用。
DOI：
10.1109/cvpr.2006.20
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发表时间：
2006
期刊：
影响因子：
--
通讯作者：
Vemuri,BabaC
中科院分区：
文献类型：
--
作者：
Banerjee,Arunava;Kodipaka,Santhosh;Vemuri,BabaC
Many problems in computer vision involving recognition and/or classification can be posed in the general framework of supervised learning. There is however one aspect of image datasets, the high-dimensionality of the data points, that makes the direct application of off-the-shelf learning techniques problematic. In this paper, we present a novel concept class and a companion tractable algorithm for learning a suitable classifier from a given labeled dataset, that is particularly suited to high-dimensional sparse datasets. Each member class in the dataset is represented by a prototype conic section in the feature space, and new data points are classified based on a distance measure to each such representative conic section that is parameterized by its focus, directrix and eccentricity. Learning is achieved by altering the parameters of the conic section descriptor for each class, so as to better represent the data. We demonstrate the efficacy of the technique by comparing it to several well known classifiers on multiple public domain datasets.
