关键词: 模糊支持张量机, 张量图像, 图像分类, 羽绒识别

Abstract: In small sample image classification application, most of traditional classification models take vectors as inputs, which may cause many defects and influence the classification performance. In this paper, the classifier of Fuzzy Support Tensor Machine (FSTM) based on tensor theory and fuzzy support vector machine was proposed. This algorithm took tensors as inputs to obtain the optimal tensor plane. After verifying the performance of the algorithm by using handwritten digital image database, FSTM was applied to triangle node of feather and down category recognition. Compared with the traditional algorithms, FSTM achieves approximately 6.3% increase in correct recognition rate on average. The experimental results show that the FSTM classifier is much more suitable for the application of image classification.

Key words: Fuzzy Support Tensor Machine (FSTM), tensor image, image classification, feather and down category recognition