Two-Dimensional Soft Linear Discriminant Projection for Robust Image Feature Extraction and Recognition

Abstract

In this study, we propose a Robust Soft Linear Discriminant Projection (RS-LDP) algorithm for extracting two-dimensional (2D) image features for recognition. RS-LDP is based on the soft label linear discriminant analysis (SL-LDA) that is shown to be effective for semi-supervised feature learning, but SLDA works in the vector space and thus extract one-dimensional (1D) features directly, so it has to convert the two-dimensional (2D) image matrices into the 1D vectorized representations in a high-dimensional space when dealing with images. But such transformation usually destroys the intrinsic topology structures of the images pixels and thus loses certain important information, which may result in degraded performance. Compared with SL-LDA for representation, our RS-LDP can effectively preserve the topology structures among image pixels, and more importantly it would be more efficient due to the matrix representations. Extensive simulations on real-world image datasets show that our proposed RS-LDP can deliver enhanced performance over other state-of-the-arts for recognition.