Feature-level fusion of deep convolutional neural networks for sketch recognition on smartphones

Abstract

Understanding hand-free sketches with automated methods is a challenging task due to the diversity and abstract structures of the sketches. In this study, we propose a robust fusion scheme, namely feature-level fusion that use deep convolutional neural networks (CNNs) for recognizing hand-free sketches and develop a sketch recognition application for smartphones based on client-server application architecture. We employ inter-layer CNN features to capture different levels of abstractions of sketches along with fusion operator. Our results on TU-Berlin hand-free sketch benchmark dataset show that, our proposed feature-level fusion scheme achieves a recognition accuracy of 69.175%. This result is promising when compared with the human recognition accuracy of 73.1% on the same dataset.