Announcements. Recognition II. Computer Vision I. Example: Face Detection. Evaluating a binary classifier

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1 Aoucemets Recogto II H3 exteded to toght H4 to be aouced today. Due Frday 2/8. Note wll take a whle to ru some thgs. Fal Exam: hursday 2/4 at 7pm-0pm CSE252A Lecture 7 Example: Face Detecto Evaluatg a bary classfer For a detector, there are two types of errors: False Postves, False accept (e.g., o-face s detected as a face) False Negatves, False Reject (e.g., face s mssed) ROC Curve (Recever Operator Characterstc)-Plot of tradeoff betwee False Postves ad false egatves Sca wdow over mage. Classfy wdow as ether: Face No-face See also deftos of precso ad recall Face dow Classfer No-face See for example, Vola-Joes face detector OpeCV Evaluatg Mult-class classfers CIFAR 0 60,000 32x32 color mages 0 classes Evaluatg Mult-class classfers Overall accuracy Cofuso Matrx Example from Coral Reef Classfcato OHER HARD CORALS Groud ruth 2008, (83.%) CCA urf Macro Sad.5.0 Acrop Pavo Mot Pocll Port CC u Ma Sa Ac Pa Mo Po Po cro d rop vo t cll rt A rf Estmated

2 Nearest Neghbor Classfer { R j } are set of trag mages. ID = arg m dst ( R, I) j j K-th Nearest Neghbor Classfcato I x 2 R x x 3 R 2 Maxmum a posteror classfer (MAP) g j (x) = P(ω j x) = P(x ω j )P(ω j ) P(x) Classfcato: Class codtoal desty ĵ = arg max g j (x) j P(x ω ) : Class codtoal desty P(ω ) : Pror of class j Posteror Curse of Dmesoalty If we wat to buld a mmum-error rate classfer the we eed a very good estmate of P(ω x) How do we do ths? Let s say our feature space s just -dmesoal ad our feature x [ 0,] Ad let s say we have 0,000 trag samples from whch to estmate our a posteror probabltes. e could estmate these probabltes usg a hstogram whch we dvded the terval to 00 evely spaced bs.. 2

3 O average each b would have 00 samples. e could estmate P(x ω ) as the umber of samples from class that fall the same b that falls to dvded by the total umber of samples that b. But ths pla does ot scale as we crease the dmesoalty of the feature space! Let s say our feature space s just 3-D dmesoal ad our feature x [ 0,] 3 Let s say we stll have 0,000 trag samples from whch to estmate our a posteror probabltes. If we estmate these probabltes usg a hstogram whch we dvde the volume to the same wdth bs as before Dmesoalty Reducto O average each b would oly have 0.0 samples! e re ot gog be able to estmate probabltes well Dmesoalty reducto: lear projecto A -pxel mage x R ca be projected to a low-dmesoal feature space y R m by y = x where s a by m matrx. Recogto s performed usg earest eghbor R m. How do we choose a good? Drop dmesos (feature selecto) Radom projects Prcpal compoet aalyss Lear dscrmat aalyss Idepedet compoet aalyss Or eve o-lear dmesoalty reducto How do we choose a good? 3

4 Egefaces: Prcpal Compoet Aalyss () Frst Prcpal Compoet Drecto of Maxmum Varace Mea Some detals: Use Sgular value decomposto, trck descrbed text to compute bass whe <<d Sgular Value Decomposto Ay m by matrx A may be factored such that A = UΣV [m x ] = [m x m][m x ][ x ] U: m by m, orthogoal matrx Colums of U are the egevectors of AA V: by, orthogoal matrx, colums are the egevectors of A A Σ: m by, dagoal wth o-egatve etres (σ, σ 2,, σ s ) wth s=m(m,) are called the called the sgular values Sgular values are the square roots of egevalues of both AA ad A A & Colums of U are correspodg Egevectors SVD Propertes I Matlab [u s v] = svd(a), ad you ca verfy that: A=u*s*v r=rak(a) = # of o-zero sgular values. U, V gve us orthoormal bases for the subspaces of A: st r colums of U: Colum space of A Last m - r colums of U: Left ullspace of A st r colums of V: Row space of A st - r colums of V: Nullspace of A For d r, the frst d colums of U provde the best d-dmesoal bass for colums of A least squares sese. Result of SVD algorthm: σ σ 2 σ s Performg wth SVD Sgular values of A are the square roots of egevalues of both AA ad A A & Colums of U are correspodg Egevectors Ad a a = [ a a2! a][ a a2! a] = AA = Covarace matrx s: Σ = =!!!! ( x µ )( x µ ) So, gorg / subtract mea mage µ from each put mage, create data matrx, ad perform th SVD o the data matrx. Commet o mages collectos A = UΣV [m x ] = [m x m][m x ][ x ] he matrx A s sometmes called the data matrx. Colums of A are vectorzed mages. So, we have m pxels ad mages. For large mages (e.g., kxk), we ofte have more > m. Usg SVD s preferred. For CIFAR, we have m=3072, ad we have =60k ad so explct form wth covarace matrx or SVD ca work. 4

5 h SVD Ay m by matrx A may be factored such that A = UΣV [m x ] = [m x m][m x ][ x ] If m>, the oe ca vew Σ as: ' 0 here Σ =dag(σ, σ 2,, σ s ) wth s=m(m,), ad lower matrx s (-m by m) of zeros. Alteratvely, you ca wrte: A = U Σ V I Matlab, th SVD s: [U S V] = svds(a) for recogto (Egefaces) Modelg. Gve a collecto of labeled trag mages, 2. Compute mea mage ad covarace matrx. 3. Compute k Egevectors (ote that these are mages) of covarace matrx correspodg to k largest Egevalues. (Or perform usg SVD!!) 4. Project the trag mages to the k-dmesoal Egespace. Recogto. Gve a test mage, project to Egespace. 2. Perform classfcato to the projected trag mages. Egefaces: rag Images Egefaces [ urk, Petlad 0 Mea Image Bass Images Accuracy of + K-NN Dffcultes wth Projecto may suppress mportat detal smallest varace drectos may ot be umportat Method does ot take dscrmatve task to accout typcally, we wsh to compute features that allow good dscrmato ot the same as largest varace or mmzg recostructo error. 5

6 Fsherfaces: Class specfc lear projecto A -pxel mage x R ca be projected to a low-dmesoal feature space y R m by P. Belhumeur, J. Hespaha, D. Kregma, Egefaces vs. Fsherfaces: Recogto Usg Class Specfc Lear Projecto, PAMI, 997, y = x where s a by m matrx. Recogto s performed usg earest eghbor R m. How do we choose a good? & Fsher s Lear Dscrmat Betwee-class scatter th-class scatter c S = ( xk µ )( xk otal scatter c S = ( xk µ here c S B = χ ( µ µ )( µ µ ) = = xk χ = xk χ c s the umber of classes µ s the mea of class χ χ s umber of samples of χ.. k µ ) )( x µ ) = S + S B χ χ 2 µ µ µ 2 If the data pots are projected by y=x ad scatter of pots s S, the the scatter of the projected pots s S & Fsher s Lear Dscrmat Computg the Fsher Projecto Matrx (Egefaces) χ χ 2 = arg max S Maxmzes projected total scatter Fsher s Lear Dscrmat FLD fld SB = arg max S Maxmzes rato of projected betwee-class to projected wth-class scatter he w are orthoormal here are at most c- o-zero geeralzed Egevalues, so m <= c- Ca be computed wth eg Matlab fld = arg max Fsherfaces = fld Sce S s rak N-c, project trag set to subspace = arg max S spaed by frst N-c prcpal compoets of the trag set. Apply FLD to N-c S B dmesoal subspace yeldg c- dmesoal feature space. S Fsher s Lear Dscrmat projects away the wth-class varato (lghtg, expressos) foud trag set. Fsher s Lear Dscrmat preserves the separablty of the classes. Harvard Face Database 0 dvduals 66 mages per perso ra o 6 mages at 5 o est o remag mages 5 o 30 o 45 o 60 o 6

7 Recogto Results: Lghtg Extrapolato Error Rate degrees 30 degrees 45 degrees Lght Drecto Correlato Egefaces Egefaces (w/o st 3) Fsherface 7