摘要
提出一种新的支持向量机的增量学习算法,分析了新样本加入训练集后支持向量集的变化情况。基于分析结论提出一种新的学习算法。研究了基于支持向量机的山羊绒和细支绵羊毛动物纤维图像识别问题,根据山羊绒和细支绵羊毛动物纤维图像的特点,分别采用自动阈值分割和Top-Hat变换,得到纤维边缘和鳞片边缘。仿真结果表明,基于支持向量机的动物纤维图像识别率高于传统的基于人工神经网络的识别率。
In this paper we present a learning algorithm for incremental support vector machine (SVM). We analysis the possible changes of support vector set after new samples are added to training set. Based on the analysis result, an algorithm is presented. This algorithm is applied for the image recognition of two special animal fibers. With the algorithm, the useless sample is discarded and knowledge is accumulated. The experiment result shows that this algorithm is more effective than the traditional SVM while the classification precision is also guaranteed.
基金
工业控制技术国家重点实验室开放实验室研究课题资助项目(K01001)。~~
关键词
支持向量机
增量学习算法
动物纤维
图像识别
support vector machine
incremental learning algorithm
animal fiber
image recognition
