Modeling and Mining of Users’ Capture Intention for Home Videos

1. Presented by Karteek Chenna Modeling and Mining of Users’ Capture Intention for Home Videos

2. Modeling and mining of Capture Intention Objective: Enhance the experience of future browsing and enjoying the home videos, both for camcorder users and viewers

3. Related Work on Video Content analysis • Video structuring • Highlight detection • Authoring

4. Capture Intention Modeling

5. Capture Intention Modeling In ten tion : a determination to act in a certain way; a concept considered as the product of attention directed to an object or knowledge. Process of capture intention generation

6. Principles of Capture Intention Mechanism • Attention is at the nexus between stimuli and intention. • Stimuli affects the generation of intention.

7. Dimension Proposals • Scene (view size, indoor/ outdoor). • People • Object • Motion • Duration

8. Proposed Intention categories

9. Capture Intention Mining • Video Structure Decomposition. • Feature Analysis. • Intention Unit Segmentation. • Intention Classification. • Experimental Results & Evaluation.

10. Capture Intention Mining Scheme

11. Feature Analysis • Attention Energy. • Attention Pattern. • Attention Window & Stability. • Attention-Specific Features. • Content-Generic Features.

12. Attention Energy • Contrast-based static salient objects. • temporal salient objects. • Camera motion. Computation of  coefficient

13. Attention Energy • Saliency Map M: M =  + (1-) +  • Attention energy map E : E (i,j)= (i,j) * = * (1-I* ) Where (I, , ) represent Intensity inductor, Temporal Coherence Inductor, Spatial Coherence Inductor

14. Attention Pattern. Representative temporal camera patterns of a subshot

15. Content Generic Features

16. Algorithm for Intention Unit Segmentation Definitions: F: feature set of a Video. M: feature dimension. N: the number of sub-slot in a video. Algorithm: 1) Normalize each dimension of nth sub shot feature set Fn to [0,1]. 2) Concatenate Fn from N sub-shots into an M*n Matrix A. 3) Decompose A by SVD as A=U*W*Vt, where U is a( M*N ) left orthogonal matrix representing the principal component directions: W= diag(w1,w2,….,wn) is a (N*N) diagonal matrix with single values in descending order: V is a (N*N) right orthogonal matrix that expands A in terms of U. 4) Compute the Euclidean Distance between two Successive sub-shot feature set Fn and Fn+1 by Dn= sigma(l) W(l)…… 5) Detect intention unit boundary. a) if n is hot boundary then n is also an intention boundary. b) otherwise if Dn>T an intention boundary exits; otherwise no intention boundary.

17. Intention Classification • SVM based scheme. • Boosting-Based Scheme.

18. Experimental Results and Evaluation • Date sets and Intention Annotation. • Objective Evaluation. • Subjective Evaluation.

19. Subjective Evaluation User Interface for User Study. A- video Browsing. B-thumbnail panel, C- curves panel.

20. Conclusion • Better User Interaction of attention-based browsing scheme by making it more like the User interaction intention-based scheme.

21. Questions….?