Regional SVM classifiers with a spatial model for object detection

This paper presents regional Support Vector Machine (SVM) classifiers with a spatial model for object detection. The conventional SVM maps all the features of training examples into a feature space, treats these features individually, and ignores the spatial relationship of the features. The regional SVMs with a spatial model we propose in this paper take into account a 3-dimentional relationship of features. One-dimensional relationship is incorporated into the regional SVMs. The other two-dimensional relationship is the pairwise relationship of regional SVM classifiers acting on features, and is modelled by a simple conditional random field (CRF). The object detection system based on the regional SVM classifiers with the spatial model is demonstrated on several public datasets, and the performance is compared with that of other object detection algorithms.