Preprocessing-Free Surface Material Classification Using Convolutional Neural Networks Pretrained By Sparse Autoencoder
Mengqi Ji, Lu Fang, Haitian Zheng, Matti Strese, Eckehard Steinbach

Acceleration signals captured during the interaction of a rigid tool with an object surface carry relevant information for surface material classification. Existing methods mostly rely on carefully designed perception-related features or features adapted from audio processing motivated by the observed similarity between acceleration signals and audio signals. In contrast, our proposed method automatically learns features from RAW acceleration data without preprocessing. The approach is based on Convolutional Neural Networks (CNN) trained and tested on RAW data. For better performance and faster convergence of the CNN, we use the weights of a trained sparse Autoencoder (AE) to initialize the weights of the first convolution layers of the CNN. This strategy is named CNN pretrained by sparse AE (ACNN). Our classification results on a publically available Haptic Texture Database demonstrate that the proposed algorithm performs favorably against existing methods.