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Efficient Salient Region Detection with Soft Image Abstraction Presented by: Shai Krakovski

Efficient Salient Region Detection with Soft Image Abstraction Presented by: Shai Krakovski. A Work of: Ming-Ming Chen, Jonathan Warrell , Wen -Yan Lin, Shuai Zheng , Vibhav Vineet , Nigel Crook Vision Group, Oxford Brookes University. Lecture structure. Motivation Related work

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Efficient Salient Region Detection with Soft Image Abstraction Presented by: Shai Krakovski

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  1. Efficient Salient Region Detection with Soft Image AbstractionPresented by: ShaiKrakovski A Work of: Ming-Ming Chen, Jonathan Warrell, Wen-Yan Lin, ShuaiZheng, VibhavVineet, Nigel Crook Vision Group, Oxford Brookes University

  2. Lecture structure • Motivation • Related work • Proposed solution • Results • Pros & Cons. • What can be done?

  3. Motivation • Object of interest image segmentation • Adaptive compression • Object level image manipulation • Internet visual media retrieval

  4. Motivation • Object of interest image segmentation • Adaptive compression • Object level image manipulation • Internet visual media retrieval

  5. Motivation • Object of interest image segmentation • Adaptive compression • Object level image manipulation • Internet visual media retrieval

  6. Motivation • Object of interest image segmentation • Adaptive compression • Object level image manipulation • Internet visual media retrieval

  7. Related Work

  8. Itti & Koch 1998

  9. Gofferman, Zelnik-Manor& Tal 2010 • Multiscale Patch-match • Normalization by dominance proximity • Higher levelsegmentation

  10. Chang, Zhang et al 2011 • Histogram-based Contrast • Region-based Contrast

  11. Perazzi &Krahenbuhl2012 • Superpixels by K-means • Uniqueness • Distribution • .

  12. Proposed Solution

  13. Image Abstraction by GMM Input GMM Output Correlation

  14. Gaussian Mixture Models e1 mean

  15. Gaussian Mixture Models

  16. Image Abstraction by GMM Input GMM Output Correlation

  17. Correlation Output

  18. Proposed Solution

  19. Global Cues • Global Uniqueness (GU) • Color Spatial Distribution (CSD)

  20. Global Cues • Global Uniqueness (GU) • Color Spatial Distribution (CSD)

  21. Global Cues • Global Uniqueness (GU) • Color Spatial Distribution (CSD)

  22. Results

  23. Results

  24. Results

  25. Results

  26. Results

  27. Pros & Cons. Method • Cons • Only considers colors • Pros • Fast • Good results Paper • Pros • Organized • Many images • Time • Cons • Few explanations • Limitations • Nothing new

  28. What can be done? • Pre-processing of the 3 channels. • Better integration of GU and CSD. • Comparing the GMM with SVM and K-means. • Finding other\better Global Cues. • Useful application.

  29. Questions?

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