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SUN Database: Large-scale Scene Recognition from Abbey to Zoo

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. Outline. Introduction

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SUN Database: Large-scale Scene Recognition from Abbey to Zoo

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  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.

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

  7. 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.

  8. Experiments and Analysis

  9. Scene Detection • Seeing Scenes in Scenes • Multiscalescanning window approach to find • sub-scenes. (1, 0.65. 0.42)

  10. 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(Bp ∩Pgt) / area(Bp) ≧ T = 15%

  11. 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.

  12. Thank you !

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