Evaluation of Support Vector Machines Using Kernels for object detection in images

摘要

This paper presents evaluation of support vector machine for object detection in images. A support vector machine is used to classify each pixel in the image and identify region of interest based on localized color patterns. General linear transformations of the image through linear, quadratic and radial basis function kernel are considered. The main problem in this approach is high run-time complexity of SVMs. To alleviate this problem we are using multiple kernels. The main target of this evaluation is introducing an object recognition system based on the results of kernel comparisons. The comparison result shows that radial basis function produces robust and efficient object detection.