The paper "SUN Database: Large-scale Scene Recognition from Abbey to Zoo" by Jianxiong Xiao et al. introduces the extensive Scene Understanding (SUN) database, which contains 899 categories and 130,519 images. This database aims to capture a wide range of scene categories, addressing the limitations of existing databases that typically contain only a few dozen classes. The authors evaluate various state-of-the-art algorithms for scene recognition using 397 well-sampled categories and establish new performance bounds. They also measure human scene classification performance on the SUN database and compare it with computational methods. Additionally, the paper explores a finer-grained scene representation to detect scenes embedded within larger scenes. The SUN database is designed to quasi-exhaustively determine the number of different scene categories and their functionalities, providing a comprehensive resource for scene understanding research.The paper "SUN Database: Large-scale Scene Recognition from Abbey to Zoo" by Jianxiong Xiao et al. introduces the extensive Scene Understanding (SUN) database, which contains 899 categories and 130,519 images. This database aims to capture a wide range of scene categories, addressing the limitations of existing databases that typically contain only a few dozen classes. The authors evaluate various state-of-the-art algorithms for scene recognition using 397 well-sampled categories and establish new performance bounds. They also measure human scene classification performance on the SUN database and compare it with computational methods. Additionally, the paper explores a finer-grained scene representation to detect scenes embedded within larger scenes. The SUN database is designed to quasi-exhaustively determine the number of different scene categories and their functionalities, providing a comprehensive resource for scene understanding research.