MRC: The Maximum Rejection Classifier for Pattern Detection. With Michael Elad, Renato Keshet

Transcription

1 MRC: The Maimum Rejection Classifier for Pattern Detection With Michael Elad, Renato Keshet 1

2 The Problem Pattern Detection: Given a pattern that is subjected to a particular type of variation, detect occurrences of this pattern in an image. Detection should be: Accurate (small number of mis-detections/falsealarms). As fast as possible.

3 Eample Face detection in images

4 Face Eamples

5 Variations in Faces Faces may vary in their appearance: Scale Location Illumination condition Pose Identity Facial epression

6 An input Image Typical Approach Detected Face Positions Compose a pyramid with 1:f resolution ratio (f=1.) Etract blocks from each location in each resolution layer Classifier Face Finder

7 The above type of algorithm is capable of finding faces: In various scales In various locations Brute Force Search In various illumination conditions In various pose Of various identities In various facial epressions Trained Classifier

8 Compleity Searching for faces in a image, the classifier is applied 1e6 times The algorithm compleity is dominated by the classifier

9 Pattern Detection as a Classification Problem Pattern detection requires a separation between two classes: a. The Target class. b. The Clutter class. Given an input pattern Z, we would like to classify it as Target or Clutter.

10 Definition: A classifier is a non-linear parametric function C(Z,θ) of the form: ( ) n : R { + 1, 1} C Z,θ A simple eample: For blocks of 4 piels [z 1,z,z 3,z 4 ] we can define C{Z} as: C(Z,θ)= sign(θ 0 + θ 1 z 1 + θ z + θ 3 z 3 + θ 4 z 4 )

11 Geometric View C(Z) draws a separating manifold between the two classes C(Z)= R n

12 Supervised Learning: In order to obtain a precise classifier we must find a good choice of parameters θ: C { Z, θ} : R n { + 1, 1} Use eamples with known labeling to find a good set of parameters

13 Eample Based Classifiers Given two training sets: { } N { } N k k=1 We want to find a set of parameters θ such that: k k =1 C {, θ} = + 1 {, θ} = 1 k C k We "hope" that the generalization is correct.

14 Linear Classifiers The Linear Classifiers are the simplest ones. The decision is based on the projection of an input signal Z onto a kernel θ. C(Z,θ) = sign { } T Z θ θ 0 The parameters {θ,θ 0 } define a separating hyperplane.

15 T Z θ < θ 0 T Z θ > θ 0 θ θ 0

16 Designing Optimal Linear Classifiers θ 0 T θ T θ θ False Alarms Mis-Detections To Be Minimized

17 The Fisher Linear Classifiers Choose the projection kernel θ that maimizes the Mahalanobis-distance between T θ and T θ d M µ ( i, j) = i i + µ j j maimize minimize

18 The Support Vector Machine (SVM) Choose the projection kernel θ that maimizes the classes margin d min. (Vapnik-Chernoveskis 8) { } N k k 1 = { } L k k = 1 d min

19 The Support Vector Machine - Continue The support vectors are those eamples realizing the minimal distance. The decision function is composed of a linear combination of these vectors. The optimal projection that emerges turns out to be the solution of a QP problem. Generalization error is bounded, and the SVM acheives the tightest bound.

20 The Limitations of Linear Classifiers The above classifiers are suitable for linearly seperable classes (or close to this). In other cases: Generalize to non-linear classifiers. Map into higher dimensional feature space. Complicated!!

21 Face Detection - Previous Works Rowley & Kanade (98), Juel & March (96): Neural Network approach - Non linear classifier. Sung & Poggio (98): Clustering the faces/non-faces into sub-groups, and RBF classifier- Non Linear. Osuna, Freund, & Girosi (97): Support Vector Machine - Classification in high dimensional feature space. Keren & Gotsman (98): Anti-Faces method - finding a linear kernel that is orthogonal to faces and smooth.

22 Our Approach - Two Steps Back Observations: A typical configuration in Pattern Detection is that the Target class is surrounded by the Clutter class. P{Target}<<P{Clutter} Conclusions: A pdf separation is not appropriate. Clutter labeling should be performed fast.

23 Distance Definition: Define a distance of a point from a pdf p (y): ( ) D,p = y ( y) p ( y) ( µ ) y dy = + p y Consequally, we define the distance of p () from p (y): (,p ) D p = ( µ ) + p ( ) d = ( µ µ ) + +

24 (,p ) D p = ( µ µ ) + + Alternatively, we can define the proimity of p to p y : Pr o ( p,p ) = ( µ µ ) + + Note, that the distance is asymmetric. p p D(p,p ) < D(p,p ):

25 Optimal Classifier for Pattern Detection We would like to find a projection kernel θ which minimizes the overlap between p =p( T θ) and p y =p( T θ): E(θ) = P() ( µ µ ) y y + + ( ) ( ) If P()=P() we obtain the Fisher Linear Classifier. In Pattern Detection P()<<P(), hence we get: y + P µ µ y + + y θ = argmin ( µ µ ) + + y y

26 θ = argmin ( ) µ µ + + The penalty term can be minimized by two alternatives: Maimize the between class distance. Minimize while maimizing y. The second alternative is more common in Pattern Detection. y y Minimize Maimize

27 Maimal Rejection The optimal θ assures that most Z are distant from. Rejection: two decision levels [d 1,d ] such that the number of rejected clutters is maimized while finding all targets. Projection onto θ 1 d d 1 Rejected points

28 Successive Rejection Following the first rejection as many as possible clutters were classified, while targets remain unclassified. In order to further reject clutter, we apply the maimal rejection technique to the remaining classes. Projection onto θ Projection onto θ 1 d d 1 Rejected points

29 Formal Derivation Formal Derivation In practice we have only samples from P and P. The class means are estimated: The class covariances are estimated: The means and variances after projection onto θ are: = k k L 1 M = k k y y L 1 M ( )( ) = k T k k M M L 1 S ( )( ) = k T k k M M L 1 S T T M θ µ and M θ µ = = θ M θ and θ M θ T T = =

30 Formal Derivation - Cont. The optimal θ minimizes the following term: ( ) E θ = ( µ µ ) y + + y = = θ T θ S θ A θ [( )( ) ] T T M M M M + S + S θ θ B θ T = θ T This term can be rewritten as a generalized eigenvalue problem: A θ = λbθ The solution is the eigenvector corresponding to the smallest eigenvalue.

31 Proposed Algorithm There are two phases to the algorithm: Training: Compute the projection kernels, and their thresholds. This process is performed ONCE and off line. Testing: Given an image, find targets using the above found kernels and thresholds.

32 Testing Stage No more Kernels Target { θ d 1, d } J j 1, = Project onto the net Kernel Is value in,d? [ d ] 1 j No Clutter es j=j+1

33 D eample falsealarmalarms 136 false- falsealarms falsealarms 5 37 falsealarms 6 35 falsealarms

34 Limitations The discriminated zone is a parallelogram polytope. Thus, if the target set is non-conve, zero false alarm discrimination is impossible!! Even if the target-set is conve, convergence to zero false-alarms is not guaranteed.

35 Face Detection Results Kernels for finding faces (1515) and eyes (715). Searching for eyes and faces sequentially - very efficient! Face DB: 04 images of 40 people (ORL-DB). Each image is also rotated ±5 and vertically flipped. This produced 14 Face images. Non-Face DB: 54 images - All the possible positions in all resolution layers and vertically flipped - about 40E6 non-face images.

36 Results Out of 44 faces, 10 faces are undetected, and 1 false alarm (the undetected faces are circled - they are either rotated or shadowed)

37 Results All faces detected with no false alarms

38 Results All faces detected with 1 false alarm(looking closer, this false alarm can be considered as a face)

39 Compleity For a set of 15 kernels (with appropriate decision levels), the first kernel typically removes about 90% of the piels from further consideration. Other kernels give typically a rejection factor of 50%. Thus, the algorithm requires slightly more that one convolution of the image with a kernel (per each resolution layer). The algorithm gives very good results (probability of detection and false alarm rate) for the tests we did on frontal faces in images.

40 Relation to Anti-Faces (Keren & Gotsman 98) Projection kernels are in Null() and smooth. This can be seen as a case where the pdf of the Clutter class is defined parametrically P ( ) T T Z ( D D)Z Z e where D is a derivative operator. In this case S =(D T D) -1, where in the Fourier basis (D T D) -1 diag(1, 1/, 1/3, ) If M M y, minimizing the following term tends to give θ Null(S ), and smooth: E ( θ ) = θ T θ [ T ( M M )( M M ) + S + S ]θ T S θ

41 Conclusions MRC: projection onto pre-trained kernels, and thresholding. The process is a rejection based classification. Appropriate for pattern detection where pdf separation is impossible. Eploits the fact that P(clutter)>>P(target) Gives a very fast clutter labeling at the epense of slow target labeling. Can also deal with non linearly separable classes (convely separable). Simple to apply (linear), with promising results for facedetection in images. A generalization of the Fisher Linear Classifier. END