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Scene Classification

Scene Classification. Pulkit Agrawal Y7322 BVV Sri Raj Dutt Y7110 Sushobhan Nayak Y7460. Outline. What is a scene Scene recognition Method Results Future Work References. What is a Scene? . Scene- as opposed to ‘object’ or ‘texture’

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Scene Classification

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  1. Scene Classification 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 • Experimental evidence • ‘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 Required Classes Image = Feature Vector() Linear Discriminant Analysis Discriminating Vector (D.V) Maximum Separation b/w classes

  13. Mathematical Details….. • Image represented as Feature Vector x. • m1 , m2: mean vector of feature vector of 2 classes

  14. Mathematical Details… • gn= feature • Gn = Gabor filter • dn = 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 • Natural • 80 Test Images • 75 classified Correctly • Artificial • 80 Test Images • 67 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/spatialenvelope/

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