1. ! ! ! ! ! ViEW2013 2013126! () /

2. SIFT[2009]SSII2007 /!2 3. SIFT[Lowe2004], ICCV1999 DoG-kk 3 0-k 2 0-k 00 DoGDoG /!3 4. SIFT[Lowe2004] 128 8 4! ! ! ! ! !4 () /!4 5. 1. SIFT-! 2. --! 3. -B-HOG /!5 6. 1. SIFT -- /!6 7. Harris HarrisH = g( )2 Ix (x)Ix Iy (x) 2 Ix Iy (x) Iy (x)Ix ()x1Iy ()y1 : : g()12R = det(H)ktr(H)2(k = 0.04 0.06)EdgeRCorner0 (, ) R > >>) R >> 0 (, ) FlatEdge /!7 8. FAST:Features from Accelerated Segment Test [Rosten2010] p 16 p n / 9. 3 2 /!9 10. C NC / 11. Harris, FAST, FAST(Pyramid)FASTHarrisFAST(Pyramid)8016428520.6 [ms]0.6 [ms]1.5 [ms] /!11 12. 1: 2.1 /2.2!12 13. SIFT /!13 14. SIFT128 8bit (unsigned char) 01 XOR010010000 010010101 000000101: 2SSE /!14 15. [SSE4.2(_mm_popcnt_u64)] [8bitLUT] 264 128256512 128 050100150 200 [ns]250300350(CPU : Intel Xeon X7452 2.67GHz)SSE4.2(popcnt)10 /!15 16. BRIEF, BRISK, ORB, FREAK ! CARD, D-BRIEF, BinBoost /!16 17. BRIEF[Calonder2010] (2) / !17 18. () () () ! : ORBFREAK : D-BRIEFBinBoost /!18 19. ORB[Rublee2011] Greedy Algorithm 300,000 (205,590) 256 / !19 20. BRIEF, BRISK, ORB, FREAK ! CARD, D-BRIEF, BinBoost /!20 21. () WT (B bit)((D)= sgn WT bxB 1)D 1B DD=136, B=128bitsB D17,408(D-1) B17,280 WT /!21 22. M-M CARD[Ambai2011] 910 2 136d128bb WT web W{-1,0,+1} W090% b/sgn PfPW Td+cQQd D b B c W D 17,4080fP}Q d 17,280 1,740 /!22 23. D(Discriminative)-BRIEF [Trzcinski2012] /!23 24. D-BRIEF[Trzcinski2012] w0 i x P N /!24 25. D-BRIEF[Trzcinski2012] w0 w! ! !D : s : : L1=Ds! !D sgn /!25 26. BinBoost[Trzcinski2013] Boosting P : N: PN hd,k hd,kbd,k d / !26 27. 2 0.2860.3011.448SIFT2.035(0.181 0.154 0.483SURF0.818(0.002 0.025 0.423FAST+ORB0.450(0.018 0.022 0.137 Cascaded FAST+ORB (0.00.1770.51.01.52.02.5 [s] (CPU : Intel Xeon X7452 2.67GHz) /!27 28. SIFT () FAST ! () SSEMIRU2013SIFT http://www.slideshare.net/hironobufujiyoshi/miru2013sift /!28 29. 2. - --Joint work with: / !29 30. Amazon Flow (A9)AR http://itunes.apple.com/us/app/ow-powered-by-amazon/id474664425?mt=8 /!30 31. /!31 32. & : Binary Hashing : 01011100 1101111000011001 0101110001000100 01011101 / !32 33. y y y ! ! ! b y! 7: 7: 7: 7 7 7 2 1 2 1 2 2 1 2 2 2 /!33 34. (-1,1)(1,1) 0111y2y1b2(-1,1)(1,1)b2y1 b1(-1,-1)(1,-1) 00 10(-1,-1)(1,-1) /!34 35. 6: Binary hashing Bin hashing Iterative Quantization(ITQ)[12] D(Y,B) : 0.65 D(Y,Y) : 8 86 6 D(Y, Y) D(Y, Y) 5.65 10D(Y, Y) D(Y, Y)10 44 22 0 0 0 0D(B, Y) (4) D(Y, B) Non-optimization Optimization J() = ||Y B||2 , F 246D(B, Y) D(Y, B)810 /LN246810!35 36. - - - Iterative Quantization (ITQ) [Gong2011] SIFT 76 3, 4 /!36 37. 1SIFTMatching rate0.9Binary vs. Binary (32 bits) Binary vs. Binary (64 bits)0.8Binary vs. Binary (128 bits)0.7 Binary vs. Real (32 bits)0.6Binary vs. Real (64 bits)0.5Binary vs. Real (128 bits)0.4Binary vs. Real with opt.(32 bits)050010001500No. of matches200025003000(d) Iterative QuantizationBinary vs. Real with opt.(64 bits) Binary vs. Real with opt.(128 bits)(32bit)SIFT > Binary-Binary > Binary-Real /!37 38. 1SIFTMatching rate0.9Binary vs. Binary (32 bits) Binary vs. Binary (64 bits)0.8Binary vs. Binary (128 bits)0.7 Binary vs. Real (32 bits)0.6Binary vs. Real (64 bits)0.5Binary vs. Real (128 bits)0.4Binary vs. Real with opt.(32 bits)050010001500No. of matches2000(d) Iterative Quantization25003000Binary vs. Real with opt.(64 bits) Binary vs. Real with opt.(128 bits)(32bit)SIFT > Binary-Real+ > Binary-Binary > Binary-Real /!38 39. 1SIFTMatching rate0.9Binary vs. Binary (32 bits) Binary vs. Binary (64 bits)0.8Binary vs. Binary (128 bits)0.7 Binary vs. Real (32 bits)0.6Binary vs. Real (64 bits)0.5Binary vs. Real (128 bits)0.4Binary vs. Real with opt.(32 bits)050010001500No. of matches2000(d) Iterative Quantization25003000Binary vs. Real with opt.(64 bits) Binary vs. Real with opt.(128 bits)SIFT >> 128bit >> 64bit >> 32bit , 128bit1/8 /!39 40. 1/8101011100 1101111000011001 0101110001000100 01011101 2 /!40 41. - (64, 128) .1 lm = 4 Hare 2 [12] y b y b SVM 2 d(y , b) (5) 2 d(y , b) (5) { { HOG SVM w 2 d(y , b) b||2 = ||y b {1, 1} d(y , b) Algorithm 1 w = ||y b||2 2 SVM T T [12] SVM T = y y 2y b + bT b. = y y 2yT b + bT b.U SVM DF (x) w (5) (5) b F (x) f (x) = b x 5) 1 GPU 5) 1 b b {0, 1} b (b = b b ) (4) b {1, 1} b {1, 1} Nb i=1T i i+++D+ SVM < b+ , x > i 3 |x| 3 SVM SVM f (x) = b x NbT i ii=1Nb /!41 42. y (Algorithm1)[13] [Hare2012] k c yk cM (6)y c M, T /{1,-1}!42 43. /[Hare2012]!43 44. [Hare2012] r50 25:-1030-30-40200 -25 -50 /!44 45. Algorithm 1 [Hare2012] . .3 Require: Algorithm 1 . y, Nb 1. : y r . Require: y, Nb r 50 r = w1. : y r . r=w 25 2. w c m : 2. w c m -10 30 -30 -40 20 for i = 1 to N= do Nb do 0 for i b 1 to -25 2.1 r {-1,1} 2.1 r {-1,1} -50 mi = sign(r) m i = sign(r) 2.2 r mi 2.2 50 r mi c c 25 ci = ||b || i i=1 0 -1 1 -1 -1 1 ci = ||b262 2.3 r i || -25 rr 2.3 ci r mi end for -50 .3.1 - r ci mi r Nb return {ci }i=1 , {mi }Nb i=1 end forreturn b y 2 d(y , b) (5) y= ||y b||2 d(y , b) 2{ci }Nb , {mi }Nb i=1 i=1 c = (c1 , c2 , , ck )T Rk M ={m1 , m2 , , mk } {1, 1}Lk k (5) (6) c = (c1 , c2 , , ck )T Rk M =T (7) (5) b + bT b. = y y 2yT (5) {m1 , m2 , , mk } {1, 1}Lk k k (5) 1 2 yT b T Mb = c m b. (7) c(5) (6) y b||2b {1, 1} (7) (5) 3 y 2y b + b b. TTT /iii=1 10 (7) k k = 3!45 46. Algorithm 1 [Hare2012] .Require: y, Nb 1. : y r . r 50 r=w 25 2. w c m : 20 -10 30 -30 -40 Algorithm 1N do 0 for i = 1 to . b -25 3 Require: Algorithm 1r. y, Nb 2.1 {-1,1} -50 m Require: y, Nb 1. :sign(r) y r . i = 1. : . r y r 2.2 = w r = w r mi 50 c m 2. c2. w c m w 25 i=1 forNi > i -1 1 -1 c== c ||bi ||2Nb 25 Nbi return {ci }2.3 i r i=2 1 1 -1 -1 -1 i=1> {mi }i=1 8.8 , ci = do . i=1 0 -1 1 -1 for i 1 to Nb -1 1 ic== do i b do 1 1 -1 i ||bi || r {-1,1} 2.1 2 -25 r {-1,1} 2.1 r 2.3 mi = sign(r) -50 mi = sign(r) y 2.2 m r r ci r mi c = ci2 , ck )T Rk M = (c1 , c mi , r 50 2.2 end for (5) c = , m , ||bi ||2 {m1 c ,Nbi r251}Lk k m>} {1, 2 i=3 1 -1 1 -1 1 return= ||y r 2 2||bi k 11: B-HOG B-HOG 10(a 11: 3 2.3 r 64 L 63 = 64 T T 10 y b c Mb = B-HOG b L = (7) ci mi b. 2 r r c i mi 2 10(a)) 5] 10(b)) 10(a)) b 10(b B-HOG end for Support Vector Machine(SVM) F i=1 {ci }Nb , {mi }Nb F () 63 L = 64 63 return Support Vector Machine(SVM) i=1 i=1 2 10(a)) SVM (9) 10(b)) 10(b)) 10 (7) SVM (9) 63 y 1/10 {1,-1} c = (c1 , c2 , , ck )T Rk M = k =1/1010(b)) 3 [16] F k {m1 , m2 , , mk } {1, 1}Lk(b) = wT b T 1/10 F 64 w b (9) L = (b) = ( SVM 2 (5) (6) 1/10 b|| 2 Gradients(HOG) [14] w riented 10(a)) 64 1/10 T (7) y T 2y b + bwb. (5) k=3 M k 63 y( B-HOG HOG T T M (7) y b ci mi b. y B-HOG c Mb = 10(b)) i=1 F (b) 192 189 y F (b) 10: M M 3.3. 3 (7) 3 3 10 10: 10: VGA HOG [15] 11 k = 3.3.3 3 3.3.3 VGA 2 64 L = 3.3.2 ( k 10: !50 10: 1/10 (7) 3.3.3 PC 10(a)) 64 3.3.3 2 / HOG 51. i=110(a)) 64 r r ci mi = sign(r) m end for3.3.1 - 2.2 r 3 end for return ( 63 {ci }Nb , {m i=1 10 (7) i }Nb c 3.3.1 - i=1 ireturn {c } , k 192 {m } 189 10(b))= 3 b y c = ||b ||2 i i=1 i i i i=1 2.3 r r L = 64 (2 , , ck )T Rk M r ci mi c = (c1 , c = end for 2 d(y , b) (5) 3.3.1 - Lk {m1 , , , mk } m k i }Nb , {mi }Nb 10(a)) 264 {1, 1} T return {c i=1 1 2 k i=1 , b) = ||y b||2 (5) (6) d(y 63 ( 2 Lk 3 3 b y c = (c , c , , c ) R 2 d(y , b) (5) {m1 , m2 , b y , mk } {1, 1} T T T 1/10 2(7), c = (c1 , c , ck ) = 1922 189 (5) d(y (7) 10(b)) y y 2y b + bT b. , b) (5) {1, 1}L d(y , b) = ||y b||2 k=3,L=64 {m1, m2, , mk } (5) 2 k 3 3 (5) (5) 1 T b||2 T Td(y , b) T =y||y cT Mb = (7) 2 ci mi b. (7) = y y 2y b + b b. b (5) 2yT b + bi=1 T T (7) b {1, 1} = y y b. (5) k 1/10 (7) k c [ns] 3 y 10 (7) (5) 1 T (5) T 1 cT b cT= = y Mb Mb y b b {1, 1} i= k = 3 M b {1, 1} i=1 3 L = 64 ( 10 (7) 64 63 852 3 2 y k c 10 (7) 10(a)) 64 L = 64 M 63 ( 2 10(a)) 10(b)) 192 189 3 3 192 189 L = 64105 63 10: 3.3.3 3 3 10(b)) 2 10(a)) 3 63 3.3.3 1/10 (7) 1/10 10(b)) Intel Xeon CPU 2.27-GHz 3 PC 2.27-GHz Intel Xeon CPU y k c k y 3 1 PC M M 3.3.2- 1/10 3 1 1/10 87% 2 10: 3.3.3 87% 2 10: 3.3.3 y y y k Hare (Algorithm1)[13] Intel X (Algorithm1)[13] 100 32 100 32 M 3.3.2 Intel Xeon CPU 2.27-GHz PC k 78% 3.3.2 78% k c c 3 PC M 3 1 87 y 10: (6) T 3.3.3 !51 (6) y c M, / Hare 87% 2 (Algorithm1)[13]y cT M, 52. 1 32 k=3k 32bit {1,-1} 256[byte]15[byte](32 8byte)3 1byte+3 4byte) /!52 53. [ns] -- ()- ()3214.2433.554.56427.6852.3105.712861.91683.2177.512% [MB] 32 641281M26.7122.138.1244.161.0488.310M2671220.7381.52441.4610.44882.82670.312207.03814.724414.16103.548828.1100M21% /!53 54. - 1/8! () (12) (21) /!54 55. 3. -B-HOG-Joint work with: OB / !55 56. () / SVM 57. () ! ! ! SVM 2(VGA)2 SVM /HOG !57 58. SVM SVM ! ! ! : (N) (HOGN ) /!58 59. (SVM) SVM B-HOG B-HOG ={0,1,0,1,...} / SVM !59 60. [16] FHOGB-HOG [2011] 4.1 B-HOG SVM Histograms of Oriented Gradients(HOG) [14] w B-HOG [15] 1 011 HOG 0 1 HOG 2 HOG 2 HOG 2 3,360 bit (=HOG) D (8) ( HOG ) c v c = {vc (1), vc (2), ..., vc (N )} bc = {bc (1), bc (2), ..., bc (N )} N th bc (n) !(8 8) = 1 0if vc (n) > thc (n)1otherwise8 F (b) 4.2(8)01111100thc (n) B-HOG HOG ( B-HOG ) HOG B-HOG b = {b1 , b2 , , bc } 64 128 8 /!60 61. b B-HOG F (b) SVM w B-HOGSVM c F [13] Hare Machine(SVM) Support Vector ()m Nb b w (9) i F (b) f (b) = i=1 ci m b SVM w m m+ {0, 1}D wcm SVM w c D m+ {0, 1}4.2.1 (m = m+ m+ ) D m {1, 1} SVM F (b) (9) SVM SVM : (10) F (b) = wT b (9) 4.2.1 SVM F (b) SVM b w w B-HOG m (9) SVM F (b) c b w Nb w F (b) f (b) = Nb i=1 ci mi b Hare [13] : f (b) = ci mi b m m+ {0, 1}D b w i=1 m+ {0, 1}D (m = m+ + Hare B-HOG [13] m ) b NbSVM w (10) SVM w c + D = >) (< {1, , b > < m+ , SVM im m 1} b cF (b) i i F SVM w (b) SVM w c i=1 D c m {1,Nb N 1} SVM m b VGA f (b) = = N ci w b F (b) SVM miF (b) f (b) + i=1 ci mi b b= ci (2 < mi , b > |b|) m m+ {0, 1}D i=1 c m /!61 62. b B-HOG 2 1 m m+ {0, [13] Hare 3, 360 m+ {0, 1}D (m = m+ m+ ) 4.2.1 port Vector Machine(SVM) F () SVM b 8 (10) SVM w SVM (9) (9) SVM F (b) HOG 11: B-HOG HOG b SVM w : w c 1/8 SVM B-HOG : 11: Nb B-HOG b [15] m {1, 1}D SVM f (b) = ci mi b F (b) = wT b (9) Vector Machine(SVM) SupportSVM w F (b) Hare [13] i=1 B-HOG b bSVM (9 4.2.1 b N w F () w m Support Vector Machine(SVM) c i = = Nib(< m+ , b > < m+ , b > c F (b) f F (b) ci mi b (b) 11: B-HOG i i=1 (9) SVM (9) SVM i=1 SVM w c [16] m m+ {0, 1}D {1,-1} Nb b w B-HOG B-HOGSVM b F+ = wT (b) m D {1, 1}D + + + B-HOG b (m = mc (2 m m m = G SVM F {0, 1}SVM w < ) i , b > |b|) i (b) F (b) i=1 rt Vector Machine(SVM) F () B-HOG wT b (10) SVM F (b) = (9) w of Oriented Gradients(HOG) [14] c m Nb SVM (9)F (b) (10) < 11: B-HOG f (b) = i=1 c mi b m Hare i[13] VGA w m m+ {0, 1}D 3 b D B-HOG HOG w B-HOG b Nb + = m+ m+ ) m {0, 1} (m c m i b = i T f (b) F (b) F () pport Vector Machine(SVM) (10) i=1 F (b) = w b (9) SVM B-HOG SSE4 SVM SVM (9) c w Nb F (b) CPU [15] {1, 1}D SVM VGA w POPCNT, B-HOG m 3000) 11 i N= ci (< m+ , b > < m+ b >) b i b(D= , SVM F (b) f (b) = F (b) SVM w i=1 ci mi b 2 (9) b F (b) = wT i=1 VGA Nb m SVM 12 c B-HOG Nb 13 =c (< m+i (2> m+ b, > >) c, , (10) Nb b < < 1} {0,1 i i Nb i=1 Nb= i=1f (b)=i m+ , b >)ci mi bi=1ci (2 < m+ ,Nb > |b|) i b =i=1(10)i ci (< m+ , b > < m+ , b >) iNb (10) < m+ , b > i=ci (2 |b|)3 i=1 /( + !64 65. + m+ D m {1, 1}D D (m = m+ m+ ) SVM + + {0, 1} m {0, 1} (m = ) SVM F (b) m m w SVM (10) SVM (10) SVM c m Nb SVM (b) f (b) = i=1 ci mi b F + Nb m m {0, 1}D Nb f (b) = m+cmi b D (m = m+ m+ ) {0, 1} f (b) = ci mi b i i=1 SVM (10) i=1 = =Nb Nb i=1 i=1 Nb Nb i=1 i=1+ + i ci (< m+ , b > < m+ , b >) i i i i Nbf (b)=ci mi bi=1 i = ci ( < m+ , b >) m+ , i i=1+ ci (2 < m+ ,Nb > |b|) i b i i(10)+ + Nb (10) < mi , b > (10) + (2 < m , b > |b|) = ci i 3 i=1 POPCNT / ( + !65 66. 0.0-6.002 /468 6.010!66 67. 0.06.0 -6.00246810 /!67 68. c m Nb 12: SVM F (b) Nb f (b) = i=1 ci mi b [2013] SVM m m+ {0, 1}D f (b) = ci mi b HOG 4.2.1 11: B-HOG + D i=1 + + m {0, 1} (m = m m ) 2 Nb 13 b (10) SVM B-HOG(9) SVM + [15] + , b >) = ci i: , i (< m b > < m b Support Vector Machine(SVM) {1,-1} i=1 Nb Nb SVM 4.2.1 f (b) = = mb i ci (2i< m+ , b > |b|) (10) SVM i=0 (a)(b) i=1 i=1(9) SVM F (b) Hare [16] Nb B-HOG b w 13 F (b) = wT b f(b) + + < m+ , b > (10) , b >)i =SVMc < mi (< mi , b > i B-HOG i=1 3 w (Nb grams of Oriented= 16) Gradients(HOG) [14] Nb SVM [13] = Hare 14 ci (2 < m+ , b > |b|) (10) (a)(b) i m {1, i=1 b BHOGSSE4.2 w F (b) CPU POPCNT 4.3 mF (b) SVM ( (10) < + , b > i SVM 3w c c (Nb = 16) D m 11 F (b) f B-HOG 12 SVM SVM [15] {1, 1} HOG (b) 14 F (b) SVM w HOG 2 SSE4.2 B-HOG c m 4.3m+ {0, 1}D (m CPU POPCNT Nb 12: F (b) f (b) = i=1 !68 HOG 2 ci mi b / (10) S 69. c m Nb 12: SVM F (b) Nb f (b) = i=1 ci mi b [2013] SVM m m+ {0, 1}D f (b) = ci mi b HOG 4.2.1 11: B-HOG + D i=1 + + m {0, 1} (m = m m ) 2 Nb 13 b (10) SVM B-HOG(9) SVM + [15] + , b >) = ci i: , i (< m b > < m b Support Vector Machine(SVM) {1,-1} i=1 Nb Nb SVM 4.2.1 f (b) = = mb i ci (2i< m+ , b > |b|) (10) SVM i=1 (a)(b) i=1 i=1(9) SVM F (b) Hare [16] Nb B-HOG b w 13 F (b) = wT b f(b) + + < m+ , b > (10) , b >)i =SVMc < mi (< mi , b > i B-HOG i=1 i=10.560.56 3 w (Nb grams of Oriented= 16) Gradients(HOG) [14] Nb SVM [13] = Hare 14 ci (2 < m+ , b > |b|) (10) (a)(b) i m {1, i=1 b BHOGSSE4.2 w F (b) CPU POPCNT 4.3 mF (b) SVM ( (10) < + , b > i SVM 3w c c (Nb = 16) D m 11 F (b) f B-HOG 12 SVM SVM [15] {1, 1} HOG (b) 14 F (b) SVM w HOG 2 SSE4.2 B-HOG c m 4.3m+ {0, 1}D (m CPU POPCNT Nb 12: F (b) f (b) = i=1 !69 HOG 2 ci mi b / (10) S 70. c m Nb 12: SVM F (b) Nb f (b) = i=1 ci mi b [2013] SVM m m+ {0, 1}D f (b) = ci mi b HOG 4.2.1 11: B-HOG + D i=1 + + m {0, 1} (m = m m ) 2 Nb 13 b (10) SVM B-HOG(9) SVM + [15] + , b >) = ci i: , i (< m b > < m b Support Vector Machine(SVM) {1,-1} i=1 Nb Nb SVM 4.2.1 f (b) = = mb i ci (2i< m+ , b > |b|) (10) SVM i=2 (a)(b) i=1 i=1(9) SVM F (b) Hare [16] Nb B-HOG b w 13 F (b) = wT b f(b) + + < m+ , b > (10) , b >)i =SVMc < mi (< mi , b > i B-HOG i=1 i=10.560.56 3 w (Nb grams of Oriented= 16) Gradients(HOG) [14] Nb SVM i=20.880.32 |b|) [13] = Hare 14 ci (2 < m+ , b > (10) (a)(b) i m {1, i=1 b BHOGSSE4.2 w F (b) CPU POPCNT 4.3 mF (b) SVM ( (10) < + , b > i SVM 3w c c (Nb = 16) D m 11 F (b) f B-HOG 12 SVM SVM [15] {1, 1} HOG (b) 14 F (b) SVM w HOG 2 SSE4.2 B-HOG c m 4.3m+ {0, 1}D (m CPU POPCNT Nb 12: F (b) f (b) = i=1 !70 HOG 2 ci mi b / (10) S 71. c m Nb 12: SVM F (b) Nb f (b) = i=1 ci mi b [2013] SVM m m+ {0, 1}D f (b) = ci mi b HOG 4.2.1 11: B-HOG + D i=1 + + m {0, 1} (m = m m ) 2 Nb 13 b (10) SVM B-HOG(9) SVM + [15] + , b >) = ci i: , i (< m b > < m b Support Vector Machine(SVM) {1,-1} i=1 Nb Nb SVM 4.2.1 f (b) = = mb i ci (2i< m+ , b > |b|) (10) SVM i=3 (a)(b) i=1 i=1(9) SVM F (b) Hare [16] Nb B-HOG b w 13 F (b) = wT b f(b) + + < m+ , b > (10) , b >)i =SVMc < mi (< mi , b > i B-HOG i=1 i=10.560.56 3 w (Nb grams of Oriented= 16) Gradients(HOG) [14] Nb SVM i=20.880.32 |b|) [13] = Hare 14 ci (2 < m+ , b > (10) (a)(b) i m {1, i=1 b BHOGSSE4.2 w i=31.060.18 F (b) CPU POPCNT 4.3 mF (b) SVM ( (10) < + , b > i SVM 3w c c (Nb = 16) D m 11 F (b) f B-HOG 12 SVM SVM [15] {1, 1} HOG (b) 14 F (b) SVM w HOG 2 SSE4.2 B-HOG c m 4.3m+ {0, 1}D (m CPU POPCNT Nb 12: F (b) f (b) = i=1 !71 HOG 2 ci mi b / (10) S 72. c m Nb 12: SVM F (b) Nb f (b) = i=1 ci mi b [2013] SVM m m+ {0, 1}D f (b) = ci mi b HOG 4.2.1 11: B-HOG + D i=1 + + m {0, 1} (m = m m ) 2 Nb 13 b (10) SVM B-HOG(9) SVM + [15] + , b >) = ci i: , i (< m b > < m b Support Vector Machine(SVM) {1,-1} i=1 Nb Nb SVM 4.2.1 f (b) = = mb i ci (2i< m+ , b > |b|) (10) SVM i=3 (a)(b) i=1 i=1(9) SVM F (b) Hare [16] Nb B-HOG b w 13 F (b) = wT b f(b) + + < m+ , b > (10) , b >)i =SVMc < mi (< mi , b > i B-HOG i=1 i=10.560.56 3 w (Nb grams of Oriented= 16) Gradients(HOG) [14] Nb SVM i=20.880.32 |b|) [13] = Hare 14 ci (2 < m+ , b > (10) (a)(b) i m {1, i=1 b BHOGSSE4.2 w >th i=31.060.18 F (b) CPU POPCNT 4.3 mF (b) SVM ( (10) < + , b > i i=41.150.09 SVM 3w c c (Nb = 16) D m 11 F (b) f B-HOG 12 SVM SVM [15] {1, 1} HOG (b) 14 F (b) SVM w HOG 2 SSE4.2 B-HOG c m 4.3m+ {0, 1}D (m CPU POPCNT Nb 12: F (b) f (b) = i=1 !72 HOG 2 ci mi b / (10) S 73. [2013] 2.19+2.05 0.21=2 0 0 -2.25 /-2.075 5 -0.1810 10 !73 74. [2013] 2.19 2.19+ +2.05=2 =40.21 0 0 0 0-2.25 /55 -2.07 -0.18 1010 !74 75. [2013] 2.19+2.05 2.05=2 0.21 =4 0 0 -2.25 /-2.075 5 -0.18 10 10 !75 76. [2013] 2.19+2.05=2 0.21=4 0 =10-2.25 /-2.075 -0.18 10 !76 77. [2013] 2.19+2.05=2 0.21=4 0 =10-2.25 /-2.075 -0.18 10 !77 78. [2013] 2.19+2.05=2 0.21=4 0 =10-2.25 /-2.07=3 5 -0.18 10 !78 79. SVM17 /!79 80. .01.0() 1.01.0 1.0.50.5 0.50.5 0.5.00.0 0.00.00.0 1 12 23 341 45 3564 67 6787 89 991010 1212 12 1514 1515 11 12 13 14 15 16 16 1 12 23 34 2 5 56 5 7 78 8 9 10 10 1113 12 1316 14 15 16 11 13 4 6 8 910 11 11 14 13 14 16 =1 =1 =1 =1 =1 =2 =2 =2 =2 =2 2=16 =16 =16 =16 =16 16=16 =16 =16 =16 =16 1690%1 /!80 81. B-HOG 2100.0HOG+SVM B-HOG+SVMth1HOG [%] ( HOG )10.0801111100( B-HOG ) 1.0 0.010.1 [%]1.010.0DET B-HOG0.1%5% /!81 82. HOG HOG CoHOG [Watanabe2009] Joint HOG[2009] Boosing CoHOGJoint HOG /!82 83. B-HOG A BC DB-HOG ( ) B C D A 01011010110100110110101011100001 A XOR B 10001001A XOR C 00110000A XOR D 10111011B-HOG 2 0101101011010011...1000100100110000... B-HOG ( 8,400 bit ) /!83 84. B-HOG A BC DB-HOG ( ) B C D A 01011010110100110110101011100001 A XOR B 10001001A XOR C 00110000A XOR D 10111011B-HOG 2 0101101011010011...1000100100110000... B-HOG ( 8,400 bit ) /!84 85. 100.0 [%]HOG+SVM B-HOG+SVM10.01.0 0.010.1 [%] /1.010.0!85 86. 100.0 [%]HOG+SVM B-HOG+SVM ( AND ) ( OR ) ( XOR )10.01.0 0.010.1 [%]1.010.0XOR8.1% /!86 87. SVM 17! B-HOG (XOR) 8.1% /!87 88. 63 10(b)) 192 m+ {0, 1}D (m = m+ m+ ) 3 3 3 [2013] (10) SVM 1/10 3{-1, 0, 1} y k c M Nbf (b) := 3ci mi b 10: 3.3.3i=13.3.2Nbi=1 1-1-1-1 y Nb-1Hare (Algorithm1)[13] 1 -1 -1 1= M 8.84.9-1229-29 -39i=1y cT M, 22 1 0 1 -1 -1 87% Hare (A 100 32 -1 0 0 -1 ci (2 < m+ , b -1k > |b|) i k c 26 Intel Xeon CPU 2.27 3.3.2 PC 3 1 i =1 ci (< m+ , b > < m+ , b >) -1 1 -1 i1 1 10: M 30(6) 10 y -10 30 -30 -40 20 cT M, (10) < m+ , i2 {-1, 1}3 {-1, 0, 1}3 !88 / 89. SVMwM cb = 1, 0, 0 , , 12 /!89 90. 25001.5 DFT0-1.50127251.0[s] 0255101273 25001 0-1012725547.7[s] 00127 DFT, FFT /!90 91. () FAST ! - /!91 92. 5% (IS), /!92 93. [Lowe2004] D. G. Lowe, Distinctive image features from scale-invariant keypoints , Int.Journal of Computer Vision,Vol.60, No.2, pp.91-110, 2004 [2009] , , SIFT , , Vol. 129-C, No. 5, pp. 824-831, 2009. [Rosten2010] E. Rosten, R. Porter, T. Drummond, Faster and Better: A Machine Learning Approach To Corner Detection , IEEE tran. On Pattern Analysis and Machine Intelligence, pp. 105-119, 2010. [M.Calonder2010] M. Calonder, V. Lepetit, C. Strecha, P. Fua, BRIEF: Binary Robust Independent Elementary Features , European Conference on Computer Vision, pp.778-792, 2010. [Rublee2011] E.Rublee, V.Rabaud, K.Konolige, G.Bradski ORB: an ecient alternative to SIFT or SURF , Int.Conference on Computer Vision, 2011. [Trzchinski2012] T. Tomasz, L. Vincent, Ecient Discriminative Projections for Compact Binary Descriptors , European Conference on Computer Vision, pp.228242, 2012. [Ambai2011] M. Ambai, Y. Yoshida, CARD: Compact And Real-time Descriptors , Int. Conference on Computer Vision, 2011. [Trzcinski2013] T. Tomasz, M. Christoudias, P. Fua, V. Lepetit, Boosting Binary Keypoint Descriptors , IEEE Conference on Computer Vision and Pattern Recognition, 2013.! /!93 94. [Yamauchi2013] Y. Yamauchi, M. Ambai, I. Sato, Y. Yoshida, H. Fujiyoshi, "Distance Computation Between Binary Code and Real Vector for Ecient Keypoint Matching", IPSJ Transactions on Computer Vision and Applications Vol. 5, pp. 124-128, 2013. [Hare2012] S. Hare, A. Saari, P. H. S. Torr, "Ecient Online Structured Output Learning for KeypointBased Object Tracking", Computer Vision and Pattern Recognition, 2012. [2011] , , , , D, Vol.J94-D, No.8, pp.11721182, 2011. [2013] , , , , , SVM , , 2013. [Watanabe2009] T. Watanabe, S. Ito, K. Yokoi, Co-occurrence Histograms of Oriented Gradients for Pedestrian Detection , Third Pacic Rim Symposium (PSIVT 2009), pp 37-47, 2009, [2009] , , , "Joint2Boosting", , Vol. J92-D, No. 9, pp. 1591-1601, 2009. [2013] , SPADE: , 16 , 2013. /!94 95. (Hironobu Fujiyoshi) 1997 19972000 Postdoctoral Fellow 2000 2004 2006 2010 (2005)(2009)(2009) (2011,2013)ISS(2013) E-mail: hf@cs.chubu.ac.jp WEB: http://www.vision.cs.chubu.ac.jp/ Facebook: facebook.com/hironobu.fujiyoshi Twitter: @hf149 /!95 96. 3[2013]301-1-11-11-1-11mi[0][-1], [+1]-11-1-11mi[0][*()], mi0 /!96 97. webBinary Hashing b/sgn PfPW Td+cQQW : 11f () : d D b B c W B D f() W fP}Q d random projectionsc/0 very sparse random projections0 random projections W PCA hashing fP}Q iterative quantization 11) Spectral Hashing fP}Q W spectral hashing /!97 98. P c1 ,c4 , P c2(8) ,c4 , Pcj ,c3 , P c2 (10 P ci R-HOG AN (5) (7) [11] HOG OR 2 (AND) (OR) B-HOG HOG 1 0 and mul V ci ,cj = V ci V cj (5) AND P ci ,cj = P ci & P cj 2 3. 4. 1 HOG or sum P OR (6) ci ,cj = P ci | P cj V ci ,cj = V ci + V c 2 HOGj xor V sub j = |V ci V cj | XOR P ci ,cj = P ci P cj (7) 0 ci ,c xxxx/xx Vol. JxxD No. xxHOG Boosting judg [13] [14] [15] 1 4 P operator =tectionP c1 ,c4 , P c2 ,c3 , P c2 ,c4 , P c3 ,c4 } 1 (5) (7)om de- HOG approx- HOG i (2 < 4 DET (HOG ) Fig. 4ciDET curve cj (Co-occurrence of HOG features). 5 B-HOG Fig. 5 Co-occurrence representation of B-HOG feaci tures. cj 4 HOG V ci ,cj = V6% VV sum / + V ci ,cj = VOR 2 1 2 mber ofxk )mul AND (5) 0 7% 2 6 DET () Fig. 6 DET curve (Eectiveness of proposed method).!98 2 (6) 99. XOR B-HOG 7 XOR 0 1 Fig. 7Positive sample that was detected. /!99 100. 420 1050 Random 294086.57 50.989 87.63 128.059 Projection 88.69 360.219 10 Random Projection Random Projection23,520bit Fig. 10 Random projection classication accuracy. (XOR)8,400bit /!100