Data Transformation of the Histogram Feature in Object Detection

Detecting objects in images is very important for several application domains in computer vision. This paper presents an experimental study on data transformation of the feature vector in object detection. We use the modified Pyramid of Histograms of Orientation Gradients descriptor and the SVM classifier to form an object detection model. We apply a simple transformation to the histogram features before training and testing. This transformation equals a small change in the kernel function for Support Vector Machines. This change is much quicker than the χ² kernel, but obtains better results. Experimental evaluations on the UIUC Image Database and TU Darmstadt Database show that the transformed features perform better than the raw features, and this transformation improves the linear separability of the histogram feature.