摘要：With the rapid development of computer technology and multimedia technology, there are a large number of images data in our daily life. If we cannot effectively manage the images, a lot of image information will be lost. As a result, people can't timely and quickly retrieve the needed image data. At present, for the image classification optimization algorithm it mainly includes neural network, Bayesian and Fuzzy sets, etc. But these algorithms have high training complexity, low convergence speed, etc. In view of this, this paper proposed an image classification optimization algorithm based on support vector machine (SVM). When does the image classification, this study followed the following steps (1) Select the proper kernel function. (2) Determine the parameters of the kernel function through the grid search method. (3) Give the feature extraction for the image based on color and texture which will be as the input to achieve the image classification. The experimental results show that the proposed method in image classification optimization has the very high accuracy. Keywords: Support Vector Machine (SVM); image classification; feature extraction; grid research
关键词：Support Vector Machine (SVM);Image Classification;Feature Extraction;Grid Research
