RL-LOGO: Deep Reinforcement Learning Localization for Logo Recognition

• URL: http://arxiv.org/abs/2312.16792v1
• Date: Thu, 28 Dec 2023 02:44:28 GMT
• Title: RL-LOGO: Deep Reinforcement Learning Localization for Logo Recognition
• Authors: Masato Fujitake
• Abstract summary: This paper proposes a novel logo image recognition approach incorporating a localization technique based on reinforcement learning. Because there is no annotation for the position coordinates, it is impossible to train and infer the location of the logo in the image. We demonstrate that the proposed method is a promising approach to logo recognition in real-world applications.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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