A Target Recognition Algorithm Based on Support Vector Machine

In order to meet the accuracy requirement of a target recognition system, a target recognition algorithm based on support vector machine is proposed in this paper. In the algorithm, firstly, a fast image multi-threshold segmentation method is accomplished by using a novel searching path of particle swarm optimization to separate the target from the background. Then some characteristics of target samples such as moment feature, affine invariant feature and texture feature based on co-occurrence matrix are extracted. Thus, the parameter optimizing selection is achieved according to the corresponding rule. After comparing with other kernel functions, the radial basis function kernel is selected to build a target classifier for one particular typical target. Meanwhile, a BP neural network based target recognition system is implemented to facilitate comparison. Finally, the target recognition method presented in this paper is applied to the airplane recognition. The experimental results show that the algorithm given in this paper can effectively detect and recognize the image target automatically. It can be applied to both single target and multi-objective recognition. Moreover, real-time target recognition can be achieved for single target.