RL-LOGO: Deep Reinforcement Learning Localization for Logo Recognition
Masato Fujitake
This paper proposes a novel logo image recognition approach incorporating a
localization technique based on reinforcement learning. Logo recognition is an
image classification task identifying a brand in an image. As the size and
position of a logo vary widely from image to image, it is necessary to
determine its position for accurate recognition. However, because there is no
annotation for the position coordinates, it is impossible to train and infer
the location of the logo in the image. Therefore, we propose a deep
reinforcement learning localization method for logo recognition (RL-LOGO). It
utilizes deep reinforcement learning to identify a logo region in images
without annotations of the positions, thereby improving classification
accuracy. We demonstrated a significant improvement in accuracy compared with
existing methods in several published benchmarks. Specifically, we achieved an
18-point accuracy improvement over competitive methods on the complex dataset
Logo-2K+. This demonstrates that the proposed method is a promising approach to
logo recognition in real-world applications.