A novel multi-branch wavelet neural network for sparse representation based object classification

Tan-Sy Nguyen, Marie Luong, Mounir Kaaniche, Long Ngo, Azeddine Beghdadi

Abstract:

Recent advances in acquisition and display technologies have led to an enormous
amount of visual data, which requires appropriate storage and management
tools. One of the fundamental needs is the design of efficient image classification
and recognition solutions. In this paper, we propose a wavelet neural network
approach for sparse representation-based object classification. The proposed
approach aims to exploit the advantages of sparse coding, multi-scale wavelet
representation as well as neural networks. More precisely, a wavelet transform
is firstly applied to the image datasets. The generated approximation and detail
wavelet subbands are then fed into a multi-branch neural network architecture.
This architecture produces multiple sparse codes that are efficiently combined
during the classification stage. Extensive experiments, carried out on various
types of standard object datasets, have shown the efficiency of the proposed
method compared to the existing sparse coding and deep learning-based meth-
ods.