模糊支持张量机图像分类算法及其应用  

Research and application of fuzzy tensor machine image classification algorithm

作　　者：邢笛[1] 葛洪伟[1] 李志伟[1] 

机构地区：[1]江南大学物联网工程学院,江苏无锡214122

出　　处：《计算机应用》2012年第8期2227-2229,2234,共4页journal of Computer Applications

基　　金：国家自然科学基金资助项目(60975027)

摘　　要：针对在小样本图像分类应用中,以向量空间作为输入的传统分类算法的不足,提出以张量理论为基础,结合模糊支持向量机思想的基于张量图像样本的模糊支持张量机分类器,利用张量表示图像样本,求解最优张量面。通过手写体数字图像样本实验仿真,验证该算法的性能,随后将其应用到羽绒菱节图像识别中进行对比,该算法较传统算法平均高出6.3%以上的识别率。实验证明该算法更适合应用于图像样本分类识别。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.

关 键 词：模糊支持张量机 张量图像 图像分类 羽绒识别 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程]