Learning a Robust Representation via a Deep Network on Symmetric Positive Definite Manifolds

TL;DR

This paper introduces a novel end-to-end deep network that aggregates convolutional features into symmetric Positive Definite matrices using new layers, improving visual classification performance.

Contribution

It proposes a new deep network architecture with specialized layers for SPD matrix aggregation and transformation, advancing feature representation in visual tasks.

Findings

Outperforms state-of-the-art methods in visual classification

Effectively constructs compact, discriminative SPD representations

Demonstrates robustness and improved convergence in experiments

Abstract

Recent studies have shown that aggregating convolutional features of a pre-trained Convolutional Neural Network (CNN) can obtain impressive performance for a variety of visual tasks. The symmetric Positive Definite (SPD) matrix becomes a powerful tool due to its remarkable ability to learn an appropriate statistic representation to characterize the underlying structure of visual features. In this paper, we propose to aggregate deep convolutional features into an SPD matrix representation through the SPD generation and the SPD transformation under an end-to-end deep network. To this end, several new layers are introduced in our network, including a nonlinear kernel aggregation layer, an SPD matrix transformation layer, and a vectorization layer. The nonlinear kernel aggregation layer is employed to aggregate the convolutional features into a real SPD matrix directly. The SPD matrix transformation layer is designed to construct a more compact and discriminative SPD representation. The vectorization and normalization operations are performed in the vectorization layer for reducing the redundancy and accelerating the convergence. The SPD matrix in our network can be considered as a mid-level representation bridging convolutional features and high-level semantic features. To demonstrate the effectiveness of our method, we conduct extensive experiments on visual classification. Experiment results show that our method notably outperforms state-of-the-art methods.