摘要
根据图像的内容把图像划分为多个不同的类别一直是计算机视觉的一个难点。这里提出了一种多类支持向量机用于图像分类的算法,该方法主要在2类支持向量机的基础上用来构造多类分类器,用于把自然图像分成多个类别,同时研究了不同核函数的参数变化对分类效果的影响,实验证明和传统的方法相比,分类的准确性有明显的提高。
We aim to get higher accuracy of scene image classification than attainable with existing methods. We propose using multi-class SVMs (Support Vector Machines) to get this desired higher accuracy. In the full paper, we explain in much detail how to structure multi-SVMs. Here we give only a briefing. Our multi-class SVMs consist of a number of 1-v-1 classifiers and use low-level features such as representative colors and Gabor textures; we make use of relevant information in the two papers by J. Platt[3], J.H. Friedman[5] respectively to structure our multi-class SVMs. In our experiments, we used 448 scene images from http://www.project.-minerva. ex. ac. uk. In this case, multi-class SVMs became 7-class SVMs. These experiments show preliminarily: (1) that the accuracy of scene image classification can be raised from 50%-70% attainable with neural network method, which gives the best accuracy among existing methods, to 60%-80% attainable with our 7-class SVMs; (2) that both different kernel functions and different parameters in a particular kernel function give quite different results of classification.
出处
《西北工业大学学报》
EI
CAS
CSCD
北大核心
2005年第3期295-298,共4页
Journal of Northwestern Polytechnical University
基金
国家自然科学基金(60175001)资助
关键词
支持向量机
图像分类
低层特征
Classification (of information)
Color
Feature extraction
Functions
Neural networks
Structures (built objects)
Textures
Vectors
