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1. Object Recognition by Parts • Object recognition started with line segments. - Roberts recognized objects from line segments and junctions. - This led to systems that extracted linear features. . - CAD-model-based vision works well for industrial. • An “appearance-based approach” was first developed for face recognition and later generalized up to a point. • The new interest operators have led to a new kind of recognition by “parts” that can handle a variety of objects that were previously difficult or impossible. 1

2. Object Class Recognition by Unsupervised Scale-Invariant Learning R. Fergus, P. Perona, and A. Zisserman Oxford University and Caltech CVPR 2003 won the best student paper award CVPR 2013 won the best 10-year award 2

3. Goal: • Enable Computers to Recognize Different Categories of Objects in Images. 3

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5. Approach • An object is a constellation of parts (from Burl, Weber and Perona, 1998). • The parts are detected by an interest operator (Kadir’s). • The parts can be recognized by appearance. • Objects may vary greatly in scale. • The constellation of parts for a given object is learned from training images 5

6. Components • Model – Generative Probabilistic Model including Location, Scale, and Appearance of Parts • Learning – Estimate Parameters Via EM Algorithm • Recognition – Evaluate Image Using Model and Threshold 6

7. Model: Constellation Of Parts Fischler & Elschlager, 1973 Yuille, ‘91 Brunelli & Poggio, ‘93 Lades, v.d. Malsburg et al. ‘93 Cootes, Lanitis, Taylor et al. ‘95 Amit & Geman, ‘95, ‘99 Perona et al. ‘95, ‘96, ’98, ‘00 7

8. Parts Selected by Interest Operator Kadir and Brady's Interest Operator. Finds Maxima in Entropy Over Scale and Location 8

9. Representation of Appearance c1 Projection onto c2 11x11 patch Normalize PCA basis 121 dimensions was too big, so they used PCA to reduce to 10-15. c915

10. Learning a Model • An object class is represented by a generative model with P parts and a set of parameters . • Once the model has been learned, a decision procedure must determine if a new image contains an instance of the object class or not. • Suppose the new image has N interesting features with locations X, scales S and appearances A. 10

11. Probabilistic Model • X is a description of the shape of the object (in terms of locations of parts) • S is a description of the scale of the object • A is a description of the appearance of the object • θ is the (maximum likelihood value of) the parameters of the object • h is a hypothesis: a set of parts in the image that might be the parts of the object • H is the set of all possible hypotheses for that object in that image. • For N features in the image and P parts in the object, its size is O(NP) 11

12. Appearance The appearance (A) of each part p Background model has mean cbg has a Gaussian density with and covariance Vbg. mean cp and covariance VP. Gaussian Part Appearance PDF Guausian Appearance PDF Object Background 12

13. Shape as Location Object shape is represented by a joint Gaussian density of the locations (X) of features within a hypothesis transformed into a scale-invariant space. Gaussian Shape PDF Uniform Shape PDF Object Background 13

14. Scale The relative scale of each part is modeled by a Gaussian density with mean tp and covariance Up. Prob. of detection Gaussian Relative Scale PDF 0.8 0.75 0.9 Log(scale) 14

15.Occlusion and Part Statistics This was very complicated and turned out to not work well and not be necessary, in both Fergus’s work and other subsequent works. 15

16. Learning • Train Model Parameters occlusion Using EM: location appearance scale • Optimize Parameters • Optimize Assignments • Repeat Until Convergence 16

17. Recognition Make this likelihood ratio: greater than a threshold. 17

18. RESULTS • Initially tested on the Caltech-4 data set – motorbikes – faces – airplanes – cars • Now there is a much bigger data set: the Caltech-101 http:// www.vision.caltech.edu/archive.html 18

19.Equal error rate: 7.5% Motorbikes 19

20.Background Images It learns that these are NOT motorbikes. 20

21.Equal error rate: 4.6% Frontal faces 21

22.Equal error rate: 9.8% Airplanes 22

23.Scale-Invariant Cats Equal error rate: 10.0% 23

24. Scale-Invariant Cars Equal error rate: 9.7% 24

25. Accuracy Initial Pre-Scaled Experiments 25