Concept drift detection in image data stream: a survey on current literature, limitations and future directions

Abstract

Concept drift—changes in the underlying data distribution over time—poses a significant challenge to machine learning systems deployed in dynamic environments. While numerous drift detection methods have been developed for structured data such as tabular and time-series streams, concept drift in image data remains an underexplored area due to the unstructured and high-dimensional nature of visual information. This survey presents the first comprehensive review of concept drift detection methods tailored for image data streams. We propose a novel taxonomy that categorizes existing approaches based on key properties such as image feature handling, detection strategy, detection level, concept drift cause, and evaluation considerations. Through the lens of this taxonomy, we analyze 14 representative concept drift detection methods designed for image data, highlighting current approaches to the field, their strengths and limitations. Based on this analysis, we outline promising future research directions to advance the field of concept drift detection in image-based systems.