Topological structural analysis based on self-adaptive growing neural network for shape feature extraction

Abstract

Self-organizing neural network has been widely used to extract the topological structure of image shape because it features topological preservation, dynamic adaptation, clustering and dimensionality reduction. However, it is difficult to automatically extract the topology structure with an appropriate number of neurons from the complex and diverse data. In this paper, a novel self-organizing neural network called self-adaptive growing neural network (SAGNN) is proposed, which can generate an appropriate number of neurons autonomously according to the size of input data without setting the total number of neurons in advance. Firstly, Similarity Evaluation Index (SEI) is proposed to evaluate the similarity between the output network and the input space. Then, on the basis of growing neural gas (GNG) network, the SEI as a network growth control condition is introduced into the SAGNN, so that the SAGNN can grow neurons on demand until the expected quantization error is not significantly improved. Experiments involving both artificial and real data sets show that SAGNN can extract the topological structure from the unsupervised data without any prior conditions (including the appropriate number of neurons).