Shape descriptor for object classification

Abstract

In this article we propose an efficient modification of a previously published shape descriptor, which is fast and simple to compute. Proposed descriptor was developed for object classification and should be used with classifiers: SVM, KNN, etc. Object classification is a common task of computer vision, which has many applications in different areas: computer intelligence, robotic vision, smart cameras, autonomous driving, etc. Because the properties of objects are largely determined by their geometric features, shape analysis and classification are essential to almost every applied scientific and technological area. Main steps of the proposed algorithm are as follows: to find the object bounds; to smooth the bound contour using extremes based approach (if needed); to find side contour feature for each of N object rotation; to gather common feature vector; to classify object contour, using pre-trained classifier (SVM, KNN). In this work, in addition to method modifications (saving object proportions, rotation invariance, applying KNN classifier), we provide a wide comparison of our algorithm with existing approaches. The described method provided state-of-the-art performance on 100 leaves and Mpeg7 datasets, and showed good results on our own Mushroom dataset separately or together with texture or color based features.