Automatic Classification of 3D Drosophila Calyx Images

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题名:	Automatic Classification of 3D Drosophila Calyx Images
作者:	Pei-Ling Liu;Henry Horng-Shing Lu;Ann-Shyn Chiang
贡献者:	Institute of Information Science, Academia Sinica.；Institute of Statistics,National Chiao-Tung University. (Taiwan)；Institute of Biotechnology, National Tsing-Hua University. (Taiwan)
关键词:	Feature Extraction;SVM
日期:	2007-12-20
上传时间:	2010-01-12 08:23:21 (UTC+0)
出版者:	亞洲大學資訊學院;中華電腦學會
摘要:	In this research, the main purpose is the application of an automatic classification method for six kinds of 3D Drosophila Calyx Images. We have six different kinds of image data. We will use extracted features describing the spatial dispersion and connectivity of 3D olfactory neuron pathway for classification. It is worth noting that much of the image data contain redundant information so we determine the essentialness of these features by cross validation accuracy. In the leave-one-out cross validation analysis, a six-category SVM classifier is three times better than random guess. Besides, there is no evidence of over-fitting, because compared to 3D spatial RST-invariant feature set alone, the 64-view Rotational Skeleton Endpoint feature set together with it raises the accuracy rate.
關聯:	2007NCS全國計算機會議 12-20~21
显示于类别:	[資訊學院] 會議論文

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