Robust object recognition via third-party collaborative representation

A simple and effective method is proposed for object recognition via collaborative representation with ridge regression. Different from existing sparse representation and collaborative representation based approaches, the proposal does not need extensive training samples for each testing class and it is robust to localization errors and large within-class variations, thus being applicable to various real-world object recognition tasks instead of handling only the well-controlled face recognition problem. Its discriminative power is explored from a third-party dataset which can be different from the training and testing datasets, therefore, it enables using an existing dictionary for testing new data without time-consuming data annotation and model re-training. As an example, the proposal is extensively tested on the representative and very challenging task of person re-identification, defining novel state-of-the-art results on widely adopted benchmark datasets using only simple and common features.