1. SUN Database: Large-scale Scene Recognition from Abbey to Zoo Jianxiong Xiao *James Haysy Krista A. Ehinger Aude Oliva Antonio Torralba Massachusetts Institute of Technology *Brown University CVPR 2010.

2. Outline Introduction A Large Database for Scene Recognition Human Scene Classification Computational Scene Classification Scene Detection Conclusion

3. Introduction We seek to quasi-exhaustively determine the number of different scene categories with different functionalities.

4. We measure how accurately humans can classify scenes into hundreds of categories. We evaluate the scene classification performance of state of the art algorithms and establish new bounds for performance on the SUN database and the 15 scene database. We study the possibility of detecting scenes embedded inside larger scenes.

5. A Large Database for Scene Recognition We selected from the 70,000 terms of all the terms of WordNet that described scenes, places, and environments. Only color images of 200 × 200 pixels or larger were kept. Dataset reaches 899 categories and 130,519 image. And we use 397 well-sampled categories in the following evaluation.

7. Human Scene Classification Experiment on Amazon’s Mechanical Turk. We group the 397 scene categories in a 3- level tree.

10. Computational Scene Classification Image Features and Kernels – GIST : the filters are Gabor-like filters tuned to 8 orientations at 4 different scales. – HOG2x2 : gives a 31-dimension descriptor for each node of the grid. Then, 2×2 neighboring HOG descriptors are stacked together to form a descriptor with 124 dimensions. – Dense SIFT 、 LBP 、 Sparse SIFT 、 histograms 、 SSIM 、 Tiny Images 、 Line Features 、 Texton Histograms 、 Color Histograms 、 Geometric Probability Map 、 Geometry Specific Histograms.

11. Experiments and Analysis

13. Scene Detection Seeing Scenes in Scenes Multiscale scanning window approach to find sub-scenes. (1, 0.65. 0.42)

14. Test Set and Evaluation Criteria – We use 24 of the 398 well-sampled SUN categories. – In every photo we trace the ground truth spatial extent of each sub-scene. – area(B p ∩ P gt ) / area(B p ) ≧ T = 15%

15. Conclusion We have proposed a quasi-exhaustive dataset of scene categories (899 environments). Using state-of-the art algorithms for image classification, we have achieved new performance bounds for scene classification. We introduced a new task of scene detection within images.

16. Thank you !