1. Learning Techniques for Video Shot Detection Under the guidance of Prof. Sharat Chandran by M. Nithya

2. Outline Introduction Types of Shot-break Previous approaches to Shot Detection  General Approach - pixel comparison, histogram comparison…  Recent Work – Temporal slice analysis, Cue Video Our Proposed approaches  Supervised Learning using AdaBoost algorithm  Unsupervised Learning using clustering  Semi-supervised Learning combining AdaBoost & clustering Conclusion

3. Introduction  9,000 hours of motion pictures are produced around the world every year.  3,000 television stations broadcasting for twenty-four hours a day produce eight million hours of video per year. Problems: Searching the video Retrieving the relevant information Solution: Break down the video into smaller manageable parts called “Shots”

4. What is Shot?  Shot is the result of uninterrupted camera work  Shot-break is the transition from one shot to the next

5. Types of Shot-Break

6. Shot-Break Hard Cut Fade DissolveWipe

7. Hard Cut

8. Fade

9. Dissolve

10. Wipe

11. Shot Detection Methods

12. Goal: To segment video into shots Two ways: Cluster the similar frames to identify shots Find the shots that differ and declare it as shot-break

13. Pervious Approaches to Shot Detection General Approaches –Pixel Comparison –Block-based approach –Histogram Comparison –Edge Change Ratio Recent Work –Temporal Slice Analysis –Cue Video

14. Pixel Comparison Frame N Frame N + 1  x=1  y=1 | P i (x,y) – P i+1 (x,y) | D(i,i+1)= X Y XY

15. Block – Based Approach Frame N Frame N + 1 Compares statistics of the corresponding blocks Counts the number of significantly different blocks

16. Histogram Comparison

17. Edge Change Ratio

18. Comparison… MethodAdvantagesDisadvantages Pixel-Comparison Simple, easy to implement Computationally heavy, Very sensitive to moving object or camera motion Block basedPerforms better than pixel Can’t identify dissolve, fade, fast moving objects Histogram comparisonPerformance is better Detects hard-cut, fade, wipe and dissolve Fails if the two successive shots have same histogram. Can’t distinguish fast object or camera motion Edge Change RatiosDetects hard-cut, fade, wipe and dissolve Computationally heavy Fails when there is large amount of motion

19. Problems with previous approaches  Can’t distinguish shot-breaks with Fast object motion or Camera motion Fast Illumination changes Reflections from glass, water Flash photography  Fails to detect long and short gradual transitions

20. Temporal – Slice Analysis

22. Cue Video

23. Temporal – Slice Analysis

24. Cue Video Graph based approach Each frame maps to a node Connected upto 1, 3 or 7 frames apart Each node is associated with – color Histogram – Edge Histogram Weights of the edges represent similarity measure between the two frames Graph partitioning will segment the video into shots

25. Proposed Approaches

26. Use learning techniques to distinguish between shot-break and Fast object motion or Camera motion Fast Illumination changes Reflections from glass, water Flash photography

27. Supervised Learning

28. Feature Extraction 25 Primitive features like edge, color are extracted directly from the image These 25 features are used as input to next round of feature extraction yielding 25 x 25 = 625 features This 625 features can be used as input to compute 625 x 625 = 15, 625 features

29. How these features can be used to classify images?

30. Solution : Use AdaBoost to select these features. Oops!! There are 15, 625 features! Applying them to red, green and blue separately will result in 46, 875 features! Can we find few important features that will help to distinguish the images?

31. Input: (x 1,y 1 ) (x 2,y 2 ) …(x m,y m ) where x 1,x 2,…x m are the images y i = 0,1 for negative and positive examples Let n and p be the number of positive and negative examples Initial weight w 1,i = 1/2n if y i = 0 and w 1,I = 1/2p if y i = 1 For t= 1,…T: Train one hypothesis h i (x) for each feature and find the error Choose the hypothesis with low error value update the weight: w t+1,i = w t,i *  t 1-e t where e i =0,1for x i classified incorrectly or correctly  t =e t /(1-e t ) Normalize w t+1,I so that it is a distribution Final hypothesis is calculated as AdaBoost Algorithm

32. Supervised Learning Extract Highly selective features AdaBoost algorithm to select few important features Train the method to detect different shot-breaks

33. Unsupervised techniques Clustering

34. Unsupervised technique - clustering

35. Hard Cut Dissolve

36. Unsupervised technique Clustering method to cluster into shots Relevance Feedback

37. Semi-supervised Learning

38.  Combination of Supervised and Unsupervised  Few labeled data are available, using which it works on large unlabeled video Steps: AdaBoost algorithm to select features Clustering method to cluster into shots Relevance Feedback

39. Conclusion…

40. Problems with previous approaches: Can’t distinguish shot-breaks with –Fast object motion or Camera motion –Fast Illumination changes –Reflections from glass, water –Flash photography  Fails to detect long and short gradual transitions Planning to use AdaBoost learning based clustering scheme for shot-detection

41. Thank you…