1. Perceptual and Sensory Augmented Computing Discussion Session: Sliding Windows Sliding Windows – Silver Bullet or Evolutionary Deadend? Alyosha Efros, Bastian Leibe, Krystian Mikolajczyk Sicily Workshop, Syracusa, 23.09.2006

2. Perceptual and Sensory Augmented Computing Discussion Session: Sliding Windows 2 A. Efros, B. Leibe, K. Mikolajczyk What is a Sliding Window Approach? Search over space and scale Detection as subwindow classification problem “In the absence of a more intelligent strategy, any global image classification approach can be converted into a localization approach by using a sliding-window search.”...

3. Perceptual and Sensory Augmented Computing Discussion Session: Sliding Windows 3 A. Efros, B. Leibe, K. Mikolajczyk Task: Object Localization in Still Images What options do we have to choose from?  Sliding window approaches –Classification problem –[Papageorgiou & Poggio,’00], [Schneiderman & Kanade,’00], [Viola & Jones,01], [Mikolajczyk et al.,’04], [Torralba et al.,’04], [Dalal & Triggs,’05], [Wu & Nevatia,’05], [Laptev,’06],…  Feature-transform based approaches –Part-based generative models, typically with a star topology –[Fergus et al.,’03], [Leibe & Schiele,’04], [Fei-Fei et al.,’04], [Felszenszwalb & Huttenlocher,’05], [Winn & Criminisi,’06], [Opelt et al.,’06], [Mikolajczyk et al.,’06],…  Massively parallel NN architectures –e.g. convolutional NNs –[LeCun et al.,’98], [Osadchy et al.,’04], [Garcia et al.,??],…  “Smart segmentation” based approaches –Localization based on robustified bottom-up segmentation –[Todorovic & Ahuja,’06], [Roth & Ommer,’06]

4. Perceptual and Sensory Augmented Computing Discussion Session: Sliding Windows 4 A. Efros, B. Leibe, K. Mikolajczyk Sliding-Window Approaches Pros:  Can draw from vast stock of ML methods.  Independence assumption between subwindows. –Makes classification easier. –Process can be parallelized.  Simple technique, can be tried out very easily. –No translation/scale invariance required in model.  There are methods to do it very fast. –Cascades with AdaBoost/SVMs  Good detection performance on many benchmark datasets. –e.g. face detection, VOC challenges  Direct control over search range (e.g. on ground plane).

5. Perceptual and Sensory Augmented Computing Discussion Session: Sliding Windows 5 A. Efros, B. Leibe, K. Mikolajczyk Sliding-Window Approaches Cons:  Can draw from vast stock of ML methods… …as long as they can be evaluated in a few ms.  Need to evaluate many subwindows (100’000s).  Needs very fast & accurate classification  Many training examples required, often limited to low training resolution.  Can only deal with relatively small occlusions.  Still need to fuse resulting detections  Hard/suboptimal from binary classification output  Classification task often ill-defined –How to label half a car?  Difficult to deal with changing aspect ratios

6. Perceptual and Sensory Augmented Computing Discussion Session: Sliding Windows 6 A. Efros, B. Leibe, K. Mikolajczyk Duality to Feature-Based Approaches… How to find maxima in the Hough space efficiently? Maxima search = coarse-to-fine sliding window stage! Main differences:  All features evaluated upfront (instead of in cascade).  Generative model instead of discriminative classifier.  Maxima search already performs detection fusion. y s Binned accum. array y s x Refinement (MSME) y s x Candidate maxima y s Hough votes

7. Perceptual and Sensory Augmented Computing Discussion Session: Sliding Windows 7 A. Efros, B. Leibe, K. Mikolajczyk So What is Left to Oppose? 1. Feature-based vs. Window-based? 2. (Almost) exclusive use of discriminative methods 3. Low training resolutions 4. How to deal with changing aspect ratios?

8. Perceptual and Sensory Augmented Computing Discussion Session: Sliding Windows 8 A. Efros, B. Leibe, K. Mikolajczyk 1. Feature-based vs. Window-based May be mainly an implementation trade-off  Few, localized features  feature-based evaluation better  Many, dense features  window-based evaluation better  Noticed already by e.g. [Schneiderman,’04]  The trade-offs may change as your method develops… y s

9. Perceptual and Sensory Augmented Computing Discussion Session: Sliding Windows 9 A. Efros, B. Leibe, K. Mikolajczyk 2. Exclusive Use of Discriminative Methods Backprojected Hypotheses Interest Points Matched Codebook Entries Probabilistic Voting Segmentation 3D Voting Space (continuous) x y s Backprojection of Maxima p(figure) Probabilities [Leibe & Schiele,04] Gen. Model inside!

10. Perceptual and Sensory Augmented Computing Discussion Session: Sliding Windows 10 A. Efros, B. Leibe, K. Mikolajczyk Generative Models for Sliding Windows Continuous confidence scores  Smoother maxima in hypothesis space  Coarser sampling possible

11. Perceptual and Sensory Augmented Computing Discussion Session: Sliding Windows 11 A. Efros, B. Leibe, K. Mikolajczyk Generative Models for Sliding Windows Continuous confidence scores  Smoother maxima in hypothesis space  Coarser sampling possible Backprojection capability  Determine a hypothesis’s support in the image  Resolve overlapping cases

12. Perceptual and Sensory Augmented Computing Discussion Session: Sliding Windows 12 A. Efros, B. Leibe, K. Mikolajczyk Generative Models for Sliding Windows Continuous confidence scores  Smoother maxima in hypothesis space  Coarser sampling possible Backprojection capability  Determine a hypothesis’s support in the image  Resolve overlapping cases Easier to deal with partial occlusion  Part-based models  Reasoning about missing parts

13. Perceptual and Sensory Augmented Computing Discussion Session: Sliding Windows 13 A. Efros, B. Leibe, K. Mikolajczyk Sliding Windows for Generative Models Apply cascade idea to generative models  Discriminative training  Evaluate most promising features first

14. Perceptual and Sensory Augmented Computing Discussion Session: Sliding Windows 14 A. Efros, B. Leibe, K. Mikolajczyk Sliding Windows for Generative Models Apply cascade idea to generative models  Discriminative training  Evaluate most promising features first Direct control over search range  Only need to evaluate positions in search corridor  Only need to consider subset of features  Easier to adapt to different geometry (e.g. curved ground surface)  Should combine discriminative and generative elements! x s y Search corridor

15. Perceptual and Sensory Augmented Computing Discussion Session: Sliding Windows 15 A. Efros, B. Leibe, K. Mikolajczyk 3. Low Training Resolutions Many current s-w detectors operate on tiny images  Viola & Jones: 24  24 pixels  Torralba et al.: 32  32 pixels  Dalal & Triggs: 64  96 pixels (notable exception) Main reasons  Training efficiency (exhaustive feature selection in AdaBoost)  Evaluation speed  Want to recognize objects at small scales But…  Limited information content available at those resolutions  Not enough support to compensate for occlusions!

16. Perceptual and Sensory Augmented Computing Discussion Session: Sliding Windows 16 A. Efros, B. Leibe, K. Mikolajczyk 4. Changing Aspect Ratios Sliding window requires fixed window size  Basis for learning efficient cascade classifier How to deal with changing aspect ratios?  Fixed window size  Wastes training dimensions  Adapted window size  Difficult to share features  “Squashed” views [Dalal & Triggs]  Need to squash test image, too

17. A. Efros, B. Leibe, K. Mikolajczyk17 What is wrong with sliding window? Search complexity?

18. A. Efros, B. Leibe, K. Mikolajczyk18 Is there anything that cannot be done with sliding window?

19. Perceptual and Sensory Augmented Computing Discussion Session: Sliding Windows 19 A. Efros, B. Leibe, K. Mikolajczyk Sliding-Window Approaches Pros:  Can draw from vast stock of ML methods.  Simple technique, can be tried out very easily.  There are methods to do it very fast.  Good detection performance on many benchmark datasets.  Direct control over search range (e.g. on ground plane). Cons:  Need to evaluate many subwindows (100’000s).  Needs very fast & accurate classification  cascades, AdaBoost  Many training examples, often limited to low training resolution.  Can only deal with relatively small occlusions.  Still need to fuse resulting detections  Hard/suboptimal from binary classification output  Difficult to deal with changing aspect ratios

20. Perceptual and Sensory Augmented Computing Discussion Session: Sliding Windows 20 A. Efros, B. Leibe, K. Mikolajczyk So What is Left to Oppose? Feature-based vs. Window-based?  Mainly implementation trade-off… (Almost) exclusive use of discriminative methods  Why not apply generative methods instead, or combinations?  Smoother maxima in sampled 3D space.  Ability to backproject responses (top-down segmentation).  Easier to deal with partial occlusions. Low training resolutions  Only limited information content How to deal with changing aspect ratios?  E.g. front & side views of cars?  Fixed/adaptive window size?  How to share features between those?