Novel object detection and recognition system based on points of interest selection and SVM classification

Abstract Due to the progression in computer vision technology, object recognition systems have gained considerable research interest. Though there are numerous object recognition systems in the literature, there is always a constant demand for better object recognition systems. Taking this as a challenge, this work proposes a novel object recognition system based on points of interest and feature extraction. Initially, the points of interest of the image are selected by means of Derivative Kadir-Brady (DKB) detector and the neighbourhood pixels of a particular window size are selected for further processing. The gabor and curvelet features are extracted from the area of interest, followed by the Support Vector Machine (SVM) classification. The performance of the proposed object recognition system is evaluated against three analogous techniques in terms of accuracy, precision, recall and F-measure. On experimental analysis, it is proven that the proposed approach outperforms the existing approaches and the performance of the proposed work is satisfactory.