1. Pulkit Agrawal Y7322 BVV Sri Raj Dutt Y7110 Sushobhan Nayak Y7460

2. Outline What is a scene Scene recognition Method Results Future Work References

3. What is a Scene? Scene- as opposed to ‘object’ or ‘texture’ Object: when view subtends 1 to 2 meters around observer---hand distance

4. What is a Scene? observer and fixated point- >5 meters

5. Scene Recognition 2 approaches  Object recognition  Global info – details and object info ignored o Experimental evidence o ‘Gist’ of image

6. Scene Recognition Exclusive classification Structural attributes- Continuous organization of scenes along semantic axes

7. Semantic axes 2 levels:  Degree of naturalness: man-made to natural landscape Ambiguous (building in field) pictures around center

8. Semantic axes  Natural scenes- degree of openness  Artificial urban scenes- degree of verticalness and horizontalness Highways--  Highways +Tall Building---  Tall Buildings

9. Method Information at various Scales What do we Need ?? High Frequency ?Low Frequency ? Both ??

10. Feature Extraction Image Power Spectrum Gabor Filters (Scale, Orientation) Features (512 used)

11. Mathematical Details… Important data from Image power spectrum Structural discriminant feature DST=Discriminat Spectral Template- --an encoding of the discriminant structure between two image categories ‘u’ -  weighted integral of power spectrum

12. Classification Image = Feature Vector() Required Classes Linear Discriminant Analysis Discriminating Vector (D.V) Maximum Separation b/w classes

13. Mathematical Details….. Image represented as Feature Vector x. m 1, m 2 : mean vector of feature vector of 2 classes

14. Mathematical Details… g n = feature G n = Gabor filter d n = through learning

15. Learning… Projection of Training Set Image F.V. on D.V. Use LDA to determine Threshold Classifier Obtained

16. Learning

17. Work.. Artificial v/s Natural Open v/s Non Open

18. Results Artificial v/s Natural Artificial 80 Test Images 67 classified Correctly Natural 80 Test Images 75 classified Correctly 89% Correct results

19. Result

20. Future Work Arrangement in semantic axes Addition of features Depth Symmetry Contrast Ruggedness 8 category arrangement (skyscrapers, highway, street, flat building, beach, field, mountain, forest) Experiment with Haar and other filters

21. Reference Torralba A. & Olivia A., Semantic Organisation of Scenes using Discriminant Structural Templates (1999) Torralba A. & Olivia A., Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope(2001) Olivia A., Gist of the Scene http://people.csail.mit.edu/torralba/code/spatialenvel ope/ http://people.csail.mit.edu/torralba/code/spatialenvel ope/