Color Image Processing Based on Nonnegative Matrix Factorization with Convolutional Neural Network

Abstract

Although Nonnegative Matrix Factorization (NMF) has been widely known as an effective feature extraction method, which provides part-based representation and good reconstruction, there were relatively few researches using NMF for color image processing. Particularly, many studies are now using Convolutional Neural Network (CNN) in combined with Auto-Encoder (AE) or Restricted Boltzmann Machine (RBM) for learning features of color images. In this paper, we explore the ability of NMF to handle color images. Especially, a new method using NMF to learn features in CNN is proposed. In our experiments conducted on CIF ARIO, NMF shows the feasibility for reconstruction and classification of color images. Furthermore, unlike edge- or curve- shaped features learned by AE and RBM in CNN, our method provides dot- shaped features. These new types of features could be considered as basic building blocks in the lowest level of constructing images. Our results demonstrate that NMF is capable of being a supporting tool for CNN in learning features.