CSWN: A Cascaded Architecture of Separator Wavelet Networks for Image Classification

Image classification is an important task within the field of computer vision. In this paper we propose a new wavelet network classifier (WNC) based on the cascaded architecture. This classifier is characterized by its new learning approach and its novel architecture which brings a novel robust test way. So, our contributions in this paper reside in two major points. The first one is the proposition of a new training algorithm which overcomes lacuna detected in the latest version of WN learning approach. Hence, our new approach creates separator WNs discriminating classes (n -- 1 WNs to classify n classes) instead of creating a WN for each training image. This contribution makes very rapid the classification process by reducing the number of comparisons between test images WNs and training WNs. The second contribution is the proposition of a novel architecture which brings a new test approach radically different to those employed in ancient WN versions. By the new architecture which is based on the cascade notion, we aim at reducing the number of kernels employed in the approximation of test images. Experiments, using well known benchmarks, show that our new classifier is very robust and rapid compared to already existing ones.