Shape Classification Using Randomized Neural Network Descriptors Extracted from the Fourier Spectrum

The study of shape objects, in special, its description and recognition is a field of intense research in computer vision. This paper presents a novel approach for shape descriptor based on two methods: the randomized neural network (RNN), which has a single hidden neuron layer and a training algorithm much faster than the traditional backpropagation approach, and the Fourier transform, a traditional signal analysis tool that enables us to decompose a signal into its frequencies. By combining these methods, we are able to compute a robust shape signature, which is capable of recognizing very different types of images, from occluded shapes to large sets of images under affine transformations. We compared the proposed approach with several shape analysis methods. The results indicate that our proposed approach can be successfully applied in different shape analysis problems.