Denoising Latent Subspace Based Subspace Learning for Image Classification

Abstract

To solve the problem that the performance of discriminant least squares regression (DLSR) is not robust to image noise in image classification, a denoising latent subspace based subspace learning (DLSSL) image classification algorithm is proposed. This method is different from the existing classification algorithm based on regression in framework. It introduces a latent subspace in the visual space and label space, and improves the traditional one-step image classification framework to two-step, that is, noise reduction before classification. This method firstly extracts high-order features of data into latent subspace by incomplete autoencoder, then uses the high-order features for regression classification. At the same time, the distance between samples in the class is controlled by the group kernel norm constraint. The introduction of latent subspace brings more flexibility to the algorithm framework, alleviates the differences of dimensions and characteristics between visual space and label space, makes the incomplete autoencoder effective in noise reduction, and improves the robustness of classification algorithm. A number of comparison experiments are designed on the face, biometric, object and deep feature datasets. The experimental results show that the proposed algorithm has strong robustness to the noise in the image, and the obtained projection matrix is more discriminative. Compared with the related image classification algorithms, this algorithm has better performance and stronger universality. Thus it can be effectively applied to various image classification tasks.

Keywords

• autoencoder;
• subspace learning;
• low-rank;
• denoising;
• image classification