One-Class Classification: A Survey

TL;DR

This survey reviews classical and deep learning-based one-class classification methods for visual recognition, discussing their advantages, limitations, datasets, and evaluation metrics, and highlights future research directions.

Contribution

It provides a comprehensive overview of OCC techniques, comparing classical statistical and modern deep learning approaches, and identifies promising research avenues.

Findings

Deep learning methods show improved accuracy over classical approaches.

Evaluation metrics vary across datasets, affecting method comparison.

Identified key challenges and future research directions in OCC.

Abstract

One-Class Classification (OCC) is a special case of multi-class classification, where data observed during training is from a single positive class. The goal of OCC is to learn a representation and/or a classifier that enables recognition of positively labeled queries during inference. This topic has received considerable amount of interest in the computer vision, machine learning and biometrics communities in recent years. In this article, we provide a survey of classical statistical and recent deep learning-based OCC methods for visual recognition. We discuss the merits and drawbacks of existing OCC approaches and identify promising avenues for research in this field. In addition, we present a discussion of commonly used datasets and evaluation metrics for OCC.