An incremental learning approach to continuous image change detection

This paper proposes a novel incremental learning based image change detection method capable of detecting changes over image series. Given two images for change detection, an intelligent agent is trained by incremental learning on the source image. As detecting changes to target image, the agent conducts “one-step more” incremental learning on the target image to find its difference against what has been just learned from the source image. For detecting continuously changes to the third image, the agent upgrade its knowledge on the second image by performing incremental learning on top of its current knowledge. For performance evaluation, we performed extensive change detection experiments on both static images and image series. The results show that the proposed approach not only provides consistently accurate image detection, but also demonstrates substantial memory efficiency improvements when compared to existing methods.