Slides - Multimedia Computing and Computer Vision Lab

multimedia.computing.de

Slides - Multimedia Computing and Computer Vision Lab

Bundle Min-Hashing for Logo Recognition!

!"#$%&'()*+#,-.'(%/&#,'0/#&1%,"'

2345*#6/%'7)*835&-'%&6'7)*83"#,'9/:/)&'0%+.';3-:+3,-'


Contributions!

2)5=%5)&F'V+W#I"G0)-)'I4%::/XI%5)&'+>',#",/#=%4'

! Y/#46:'%&'/&:"%&I#D+%:#6'I4%::/X#,'

! (#",/#=%4'I%&'#%:/4>':I%4#'H/"1'4%,-#'&3*+#,')$'I4%::#:'

! VZ#&'$#H'",%/&/&-'/*%-#:'%=%/4%+4#'

'

Bundle Min-Hashing

1P-WGC-RANSAC

Query /

Test Image

Visual

Words

Image

Description

Querying

Re-ranking

k-nn

Classification

Synthetic Query Expansion

Index of

Reference

Images

Synthetic Database Augmentation


Spatial Re-Ranking!


Fast Spatial Re-ranking!

[#)*#",/I'=#,/XI%5)&')$',#",/#=%4',#:34":'/*8,)=#'/*%-#':#%,I1'

7)&=#&5)&%4'(;\!;7'1%:':8##6'%&6',)+3:"&#::'/::3#:'

23I1'+#E#,F'ND8)/&"D+%:#6'(;\!;7']^1/4+/&Q_`'

! S#"#,*/&/:5IF'%44'$#%"3,#'I),,#:8)&6#&I#:'>/#46'%'1>8)"1#:/:')&I#'

! 2),#',)+3:"F',#a3/,#:'$#H#,'I),,#:8)&6#&I#:.''

*)6#4'/:'I)&:",%/'")':/*/4%,/">G%b&#'",%&:$),*:'

! K&/5%44>'#:5*%"#6'",%&:$),*%5)&:'%,#',#X'+>'#:5*%5&-'$344>'

8,)W#I5=#'",%&:$),*%5)&'%:'0)I%4'V85*/c%5)&'L0VM':"#8''

L#C-C'+>'0#%:"D2#6/%&DV$D!a3%,#:M')&'/&4/#,'I),,#:8)&6#&I#:'

]^1/4+/&Q_`'^1/4+/&.'dC.'713*.'VC.'K:%,6.'2C.'!/=/I.'dC.'e/::#,*%&.';C''

!"#$%&'($&()$*+,'-)&.',+(/$'*0%+"1,+()$2'+34'5+2&'26+7+,'8+&%.)3/''L79^('PQQ_M'


Projective Transformations Need Checking!

f:5*%"#6'8,)W#I5=#'",%&:$),*%5)&:'*3:"'+#'I1#IB#6'$),':%&/">'

;'",%&:$),*%5)&'#:5*%"#6'$,)*'%':/&-4#'$#%"3,#'I),,#:8)&6#&I#'/:'

I)&:",%/'")'4#::'6#-,##:D)$D$,##6)*'"1%&'%'8,)W#I5=#'",%&:$),*%5)&'

gh'8,#=#&":'6#-#&#,%"#6',#:34":C'

'


1-Point RANSAC followed by LO!

^,)W#I5=#',#D#:5*%5)&')$'/&/5%4'",%&:$),*%5)&'6)#:'&)"'/*8,)=#',#D,%&B/&-C'

\)'%66/5)&%4'$%4:#'$#%"3,#'I),,#:8)&6#&I#:'I%&'+#'#4/*/&%"#6C'

T)8'NQ'#:5*%"#6'",%&:$),*%5)&'1>8)"1#:#:'8,)W#I"#6'/&")'I%&6/6%"#'/*%-#'

Query image RANSAC with LO step RANSAC without LO step


1-Point RANSAC followed by LO!

^,)W#I5=#',#D#:5*%5)&')$'/&/5%4'",%&:$),*%5)&'6)#:'&)"'/*8,)=#',#D,%&B/&-C'

\)'%66/5)&%4'$%4:#'$#%"3,#'I),,#:8)&6#&I#:'I%&'+#'#4/*/&%"#6C'

T)8'NQ'#:5*%"#6'",%&:$),*%5)&'1>8)"1#:#:'8,)W#I"#6'/&")'I%&6/6%"#'/*%-#'

Query image RANSAC with LO step RANSAC without LO step


1P-WGC-RANSAC!

V3,'%88,)%I1F''

ND8)/&"D+%:#6'(;\!;7'/&I),8),%5&-'H#%B'-#)*#",/I'I)&:/:"#&I>'Li[7M'

! j'6#-,##:D)$D$,##6)*'L",%&:4%5)&.':I%4/&-.',)"%5)&M'

! ^,)W#I5=#',#X&#*#&"')$'")8'1>8)"1#:#:'I%&'+#')*/E#6'L)85)&%4M'

! i[7DI)&:",%/&"':8##6:'38'1>8)"1#:/:'#=%43%5)&F'

K$'%'$#%"3,#'I),,#:8)&6#&I#'/:'&)"'i[7DI)&:/:"#&"'H/"1',#:8#I"'")'I3,,#&"'

#:5*%5)&')$'",%&:4%5)&.':I%4#'%&6',)"%5)&F'

gh'S/,#I"4>'",#%"#6'%:')3"4/#,C';=)/6:'I)*83"%5)&')$'I):"4>'#,,),'$3&I5)&C'


1P-WGC-RANSAC Experiments!

Vm$),6no'6%"%:#".'!KJT'$#%"3,#:.':/*/4%,',#:34":')&'J4/IB,0)-):DRP'

Method Voc mAP Time

[Philbin07], bow 100K 0.535 −

[Philbin07] bow+SP 100K 0.597 −

bow, tf-idf 100K 0.571 −

1p-ransac, incl.lo 100K 0.678 160s

1p-ransac, nolo 100K 0.680 72s

1p-wgc-ransac, incl.lo 100K 0.693 115s

1p-wgc-ransac, nolo 100K 0.692 53s

[Philbin07], bow 1M 0.618 −

[Philbin07], bow+SP 1M 0.645 −

[Arandjelovic12], bow 1M 0.636 −

[Arandjelovic12], bow+SP 1M 0.672 −

bow, tf-idf 1M 0.647 −

1p-ransac, incl.lo 1M 0.712 54s

1p-ransac, nolo 1M 0.711 15s

1p-wgc-ransac, incl.lo 1M 0.704 50s

1p-wgc-ransac, nolo 1M 0.703 12s

NM'V3"8#,$),*:']^1/4+/&Q_`'%&6'

];,%&6W#4)=/INP`'

]^1/4+/&Q_`'^1/4+/&.'dC.'713*.'VC.'K:%,6.'2C.'!/=/I.'dC.'e/::#,*%&.';C.'!"#$%&'($&()$*+,'-)&.',+(/$'*0%+"1,+()$2'+34'5+2&'26+7+,'8+&%.)3/'L79^(kQ_M'

];,%&6W#4)=/INP`';,%&6W#4)=/I.'(C.'e/::#,*%&.';C.'!"#$$%&"'()*%$+$#,-($%*"-./0%1(-2%&-%'34#-+$%-56$7&%#$&#'$+8/.'L79^(lNPM'


1P-WGC-RANSAC Experiments!

Vm$),6no'6%"%:#".'!KJT'$#%"3,#:.':/*/4%,',#:34":')&'J4/IB,0)-):DRP'

Method Voc mAP Time

[Philbin07], bow 100K 0.535 −

[Philbin07] bow+SP 100K 0.597 −

bow, tf-idf 100K 0.571 −

1p-ransac, incl.lo 100K 0.678 160s

1p-ransac, nolo 100K 0.680 72s

1p-wgc-ransac, incl.lo 100K 0.693 115s

1p-wgc-ransac, nolo 100K 0.692 53s

[Philbin07], bow 1M 0.618 −

[Philbin07], bow+SP 1M 0.645 −

[Arandjelovic12], bow 1M 0.636 −

[Arandjelovic12], bow+SP 1M 0.672 −

bow, tf-idf 1M 0.647 −

1p-ransac, incl.lo 1M 0.712 54s

1p-ransac, nolo 1M 0.711 15s

1p-wgc-ransac, incl.lo 1M 0.704 50s

1p-wgc-ransac, nolo 1M 0.703 12s

NM'V3"8#,$),*:']^1/4+/&Q_`'%&6'

];,%&6W#4)=/INP`'

PM'S,)88/&-'0V',#X&#*#&"'6)#:'&)"'

%p#I"'8#,$),*%&I#'L*;^M'+3"'>/#46:'

*%W),':8##638'

]^1/4+/&Q_`'^1/4+/&.'dC.'713*.'VC.'K:%,6.'2C.'!/=/I.'dC.'e/::#,*%&.';C.'!"#$%&'($&()$*+,'-)&.',+(/$'*0%+"1,+()$2'+34'5+2&'26+7+,'8+&%.)3/'L79^(kQ_M'

];,%&6W#4)=/INP`';,%&6W#4)=/I.'(C.'e/::#,*%&.';C.'!"#$$%&"'()*%$+$#,-($%*"-./0%1(-2%&-%'34#-+$%-56$7&%#$&#'$+8/.'L79^(lNPM'


1P-WGC-RANSAC Experiments!

Vm$),6no'6%"%:#".'!KJT'$#%"3,#:.':/*/4%,',#:34":')&'J4/IB,0)-):DRP'

Method Voc mAP Time

[Philbin07], bow 100K 0.535 −

[Philbin07] bow+SP 100K 0.597 −

bow, tf-idf 100K 0.571 −

1p-ransac, incl.lo 100K 0.678 160s

1p-ransac, nolo 100K 0.680 72s

1p-wgc-ransac, incl.lo 100K 0.693 115s

1p-wgc-ransac, nolo 100K 0.692 53s

[Philbin07], bow 1M 0.618 −

[Philbin07], bow+SP 1M 0.645 −

[Arandjelovic12], bow 1M 0.636 −

[Arandjelovic12], bow+SP 1M 0.672 −

bow, tf-idf 1M 0.647 −

1p-ransac, incl.lo 1M 0.712 54s

1p-ransac, nolo 1M 0.711 15s

1p-wgc-ransac, incl.lo 1M 0.704 50s

1p-wgc-ransac, nolo 1M 0.703 12s

NM'V3"8#,$),*:']^1/4+/&Q_`'%&6'

];,%&6W#4)=/INP`'

PM'S,)88/&-'0V',#X&#*#&"'6)#:'&)"'

%p#I"'8#,$),*%&I#'L*;^M'+3"'>/#46:'

*%W),':8##638'

RM'i[7'I)&:",%/&"')Z#&'>/#46:'

:4/-1"4>'+#E#,'8#,$),*%&I#'%&6'/:'

$%:"#,'

]^1/4+/&Q_`'^1/4+/&.'dC.'713*.'VC.'K:%,6.'2C.'!/=/I.'dC.'e/::#,*%&.';C.'!"#$%&'($&()$*+,'-)&.',+(/$'*0%+"1,+()$2'+34'5+2&'26+7+,'8+&%.)3/'L79^(kQ_M'

];,%&6W#4)=/INP`';,%&6W#4)=/I.'(C.'e/::#,*%&.';C.'!"#$$%&"'()*%$+$#,-($%*"-./0%1(-2%&-%'34#-+$%-56$7&%#$&#'$+8/.'L79^(lNPM'

'


1P-WGC-RANSAC Experiments!

Vm$),6no'6%"%:#".'!KJT'$#%"3,#:.':/*/4%,',#:34":')&'J4/IB,0)-):DRP'

Method Voc mAP Time

[Philbin07], bow 100K 0.535 −

[Philbin07] bow+SP 100K 0.597 −

bow, tf-idf 100K 0.571 −

1p-ransac, incl.lo 100K 0.678 160s

1p-ransac, nolo 100K 0.680 72s

1p-wgc-ransac, incl.lo 100K 0.693 115s

1p-wgc-ransac, nolo 100K 0.692 53s

[Philbin07], bow 1M 0.618 −

[Philbin07], bow+SP 1M 0.645 −

[Arandjelovic12], bow 1M 0.636 −

[Arandjelovic12], bow+SP 1M 0.672 −

bow, tf-idf 1M 0.647 −

1p-ransac, incl.lo 1M 0.712 54s

1p-ransac, nolo 1M 0.711 15s

1p-wgc-ransac, incl.lo 1M 0.704 50s

1p-wgc-ransac, nolo 1M 0.703 12s

NM'V3"8#,$),*:']^1/4+/&Q_`'%&6'

];,%&6W#4)=/INP`'

PM'S,)88/&-'0V',#X&#*#&"'6)#:'&)"'

%p#I"'8#,$),*%&I#'L*;^M'+3"'>/#46:'

*%W),':8##638'

RM'i[7'I)&:",%/&"')Z#&'>/#46:'

:4/-1"4>'+#E#,'8#,$),*%&I#'%&6'/:'

$%:"#,'

jM'(#%4D5*#'8#,$),*%&I#F'

h'jQQ'/*%-#:G:.'q'PCn'*:G/*%-#'

L:/&-4#D"1,#%6'8#,$),*%&I#'/&I4C'KGVM'

]^1/4+/&Q_`'^1/4+/&.'dC.'713*.'VC.'K:%,6.'2C.'!/=/I.'dC.'e/::#,*%&.';C.'!"#$%&'($&()$*+,'-)&.',+(/$'*0%+"1,+()$2'+34'5+2&'26+7+,'8+&%.)3/'L79^(kQ_M'

];,%&6W#4)=/INP`';,%&6W#4)=/I.'(C.'e/::#,*%&.';C.'!"#$$%&"'()*%$+$#,-($%*"-./0%1(-2%&-%'34#-+$%-56$7&%#$&#'$+8/.'L79^(lNPM'


Boosting Recall by Warping!


Boosting Recall by Warping!

K&$#,'%,5XI/%4'=%,/%5)&:'$,)*':/&-4#'/*%-#F'

[#&#,%"#'%b&#'",%&:$),*#6'=#,:/)&:')$'"1#'),/-/&%4'/*%-#'LrH%,8/&-sM'


Synthetic Query Expansion!

' SynQE

'

'

'

warped

versions

query image

multiple

queries

image

database

merging results

'

'

warped

versions

'

'#m8%&:/)&'!>&tf'%4:)'H),B:'/$'"1#'")8D*):"',#",/#=#6'

/*%-#:'H#,#'&)"',#",/#=#6'I),,#I"4>C'


Synthetic Database Augmentation!

'

SynAUG

'

'

'

'

query image

single

query

image

database

indexing

original

image

warped

versions

'

'

R ,6 'I%:#F'u)"1'!>&tf'v'!>&;


Warping Experiments!

0.65

'

'

0.60

mAP

'

0.55

'

0.50

'

'

0.45

'

0.5 0.6 0.7 0.8 0.9 0.95 1.0 (original)

α

bow, 1M

bow, 2M

SynQE, 1M

SynQE, 2M

SynAUG, 1M

SynAUG, 2M

SynQE+SynAUG, 1M

SynQE+SynAUG, 2M

u%-D)$DH),6:',#",/#=%4'/*8,)=#:':/-&/XI%&"4>'+>'!>&tf'%&6'!>&;


Bundle Min-Hashing!


Motivation 1!

V+W#I"',#",/#=%4'6)*/&%"#6'+>''

+%-D)$DH),6:'*)6#4'

'

K*84/I%5)&:')$'=/:3%4'=)I%+34%,>':/c#F'

! 7)&",)4:':",/I"&#::')$'=/:3%4'6#:I,/85)&'

! 7)&",)4:'"1#'&3*+#,')$'/*%-#:',#"3,'8#,'a3#,>'H),6''

Lg',#:8)&:#',%5)M'

'

VZ#&'=)I%+34%,>')$'N'*/44/)&'=/:3%4'H),6:'/:'3:#6F'

u%-D)$DH),6:':544',#"3,&:'+#"H##&'_QDwnx')$'"1#'6%"%+%:#y']T),,#:%&/Qw`''

(#:345&-'/::3#:F':I%4%+/4/">'%&6':8##6'

]T),,#:%&/Qw`'T),,#:%&/.'0C.'!c3**#,.'2C.'z'J/"c-/++)&.';!"""

9$+(3)3/':1$(;


Motivation 2!

V+:#,=%5)&F'

!388,#::/)&')$'$%4:#'=/:3%4'H),6:'I),,#:8)&6#&I#:''

*3I1'*),#'/*8),"%&"'"1%&',#63I/&-'a3%&5c%5)&'%,5$%I":'

'

fm%*84#:F'0%,-#'=)I%+34%,/#:.'{%**/&-'f*+#66/&-'


Idea: Feature Bundling!

;--,#-%"#'*34584#'4)I%4'$#%"3,#:'/&")''

=/:3%4'6#:I,/85)&'Lg$#%"3,#'+3&64#M'

[)%4F'

! S/:5&I5=#'6#:I,/85)&')$'/*%-#'I)&"#&"'

! !388,#::'$%4:#'8):/5=#:'63,/&-',#",/#=%4'

71%44#&-#:F'

! !"),%-#':8%I#'

! !3+D4/&#%,':#%,I1'

! ()+3:"'%88,)m/*%"#':/*/4%,/">':#%,I1'


Idea: Feature Bundling!

;--,#-%"#'*34584#'4)I%4'$#%"3,#:'/&")''

=/:3%4'6#:I,/85)&'Lg$#%"3,#'+3&64#M'

[)%4F'

! S/:5&I5=#'6#:I,/85)&')$'/*%-#'I)&"#&"'

! !388,#::'$%4:#'8):/5=#:'63,/&-',#",/#=%4'

71%44#&-#:F'

! !"),%-#':8%I#'

! !3+D4/&#%,':#%,I1'

! ()+3:"'%88,)m/*%"#':/*/4%,/">':#%,I1'

Bundle Min-Hashing

0.6x – 2.6x BoW


Bundle Min-hashing!

9

14

7

2

bundling

}

central

visual word

7


set of neighbors

2 9 14

, ,

min-hashing

}

K6#%F'T1#':8%5%4'&#/-1+),:')$'I#&",%4'$#%"3,#:'%,#'%--,#-%"#6'/&")'+3&64#:C''

f%I1'+3&64#'/:'%'r*/I,)D+%-D)$DH),6:sC'

T1#'+3&64#:'%,#'/&6#m#6'+>',#-34%,'*/&D1%:1/&-'


Min-Hash: Image Representation!

2/&D{%:1'/:'%'4)I%4/">D:#&:/5=#'1%:1/&-'"#I1&/a3#'

! ;44)H:'%88,)m/*%"#':/*/4%,/">':#%,I1')$':8%,:#':#":'

;&'/*%-#'/:'*)6#4#6'%:':#"')$'=/:3%4'H),6')II3,,#&I#:F'

'

I = {v 0 ,...,v l−1 }

!/*/4%,/">')$'/*%-#:'*)6#4#6'%:')=#,4%8'+#"H##&':#":')$'=/:3%4'H),6:'

'D'B&)H&'%:'d%II%,6':/*/4%,/">F'

'

ovr(I 1 ,I 2 )= |I 1 ∩ I 2 |

|I 1 ∪ I 2 |

0/&#%,':I%&')=#,'6%"%+%:#'")'I)*83"#'''''''''''''''''''''''/&$#%:/+4#y'

ovr(I 1 ,I 2 )

gh'2/&D{%:1'%88,)m/*%"#:'"1/:':/*/4%,/">'+3"'%44)H:':3+D4/&#%,':#%,I1'

'


Min-Hash: Definition!

;&'/*%-#'/:'*)6#4#6'%:':#"')$'=/:3%4'H),6')II3,,#&I#:F'

'

v i

{%:1'$3&I5)&'''''''''''''%::/-&:'''''''%'1%:1'=%43#'

h(v i )

D'S,%H&'6#"#,*/&/:5I%44>'$,)*'3&/$),*',%&6)*'6/:",/+35)&'

I = {v 0 ,...,v l−1 }

'

T1#'*/&D1%:1'/:'6#X'%:'' mh(I) = argmin

'

v i ∈I

h(v i )

^,)+%+/4/">')$')+"%/&/&-'/6#&5I%4'*/&D1%:1'$),'"H)'6/p#,#&"':#":F'

P (mh(I 1 )=mh(I 2 )) = ovr(I 1 ,I 2 )= |I 1 ∩ I 2 |

|I 1 ∪ I 2 |


[/=#&'"H)'/*%-#:'

'

'

Min-Hash: Example!

I 1

=

I 2 =

}

}

2 5 7

, ,

1 2 4

, ,

,

,

9

9

,

}

14

}

ovr(I 1 ,I 2 )=

|I 1 ∩ I 2 |

|I 1 ∪ I 2 | = 2 7

9/:3%4'H),6:'I)&:/6#,#6'H1#&'I)*835&-'"1#'*/&D1%:1'+>'

'

'

}

1 2 4 5 7

,

, ,

,

,

9

14 }

,

= |I 1 ∪ I 2 |

argmin

v i ∈I

h(v i )

\3*+#,')$'=/:3%4'H),6:'%88#%,/&-'/&'+)"1'/*%-#:'g''

2/&D1%:1'%::/-&:'#%I1'%'=/:3%4'H),6'%'1%:1'=%43#C'

T1#&'"1#'H),6'H/"1'"1#':*%44#:"'1%:1'=%43#'/:':#4#I"#6C'

71%&I#')$'6,%H/&-'%'*/&D1%:1'"1%"'/:'%''

=/:3%4'H),6'%88#%,/&-'/&'+)"1'/*%-#:'g'

|I 1 ∩ I 2 |

|I 1 ∩ I 2 |

'''''''''''g'#a3%4'")')=#,4%8'

|I 1 ∪ I 2 |


Min-Hash: Sketches!

;'*/&D{%:1'/:'%'=/:3%4'H),6'|'%':/&-4#'*/&D1%:1'/:'&)"'*),#'6/:5&I5=#'

"1%&'%':/&-4#'=/:3%4'H),6'

NC

PC

K&I,#%:#'6/:I,/*/&%5=#&#::F'-,)38'*34584#'*/&D1%:1#:'/&")''

#D"384#:'I%44#6''}$#%&'(%$rF' (mh 1 , . . . , mh k )

gh'S,%:5I%44>'/&I,#%:#:'8,#I/:/)&.'+3"',#I%44':3p#,:'

K&I,#%:#',#I%44F',#8#%"'8,)I#::')'5*#:'%&6'I)*83"#')':B#"I1#:C''

'$),'%':B#"I1'I)44/:/)&')$')':B#"I1#:')$':/c#'#F'

P (collision) =1− (1 − ovr(I 1 ,I 2 ) k ) n


Min-Hash: Indexing & Search!

K&6#m/&-F'

NC

PC

!#%,I1F'

f%I1':B#"I1'/:'/&:#,"#6'/&")'%'1%:1'"%+4#'+>'

1%:1/&-'"1#'#D"384#'/":#4$'/&")'1%:1'B#>:C'

T1#'*D"1':B#"I1'/:'/&:#,"#6'/&")'"1#'*D"1'1%:1'"%+4#'

1st sketch

i-th sketch

1st

hash table

( 7 , 2

( 7 , 14

(

(

i-th

hash table

NC

PC

2/&D1%:1#:'%&6':B#"I1#:'%,#'I)*83"#6'H/"1'"1#'

:%*#'1%:1'$3&I5)&:'%:'$),'/&6#m/&-'

t3#,>'*D"1'1%:1'"%+4#'/$'/"'I)&"%/&:'*D"1'a3#,>'

:B#"I1'

• K$':B#"I1'/:'I)&"%/'/&'1%:1'"%+4#'LrI)44/:/)&sM'

"1#&':/*/4%,/">')$'/*%-#:'/:'h'Q'

• (#",/#=#'%::)I/%"#6'/*%-#:'

7 2

7 14


From Min-Hashing to Bundle Min-hashing!

V+:#,=%5)&'NF'

(#-34%,'2/&D{%:1F'!B#"I1#:')$':/c#'P'8#,$),*'*3I1'+#E#,'"1%&')$':/c#'R'

r!/*/4%,s'/*%-#:.'#C-C':1)H/&-':%*#')+W#I"'3:3%44>'1%=#')=#,4%8'I4):#'")'

QC'L(#8),"#6'QCQNw'/&']0##NQ`M'

K*8),"%&"'8%,"'

P (collision) =1− (1 − ovr(I 1 ,I 2 ) k ) n


Bundle Min-hashing!

9

14

7

2

bundling

}

central

visual word

7


set of neighbors

2 9 14

, ,

min-hashing

}

K6#%F'T1#':8%5%4'&#/-1+),:')$'I#&",%4'$#%"3,#:'%,#'%--,#-%"#6'/&")'+3&64#:C''

f%I1'+3&64#'/:'%'r*/I,)D+%-D)$DH),6:sC'

T1#'+3&64#:'%,#'/&6#m#6'+>',#-34%,'*/&D1%:1/&-'


From Min-Hashing to Bundle Min-hashing!

9

14

7

2

bundling

}

central min-hashing

visual word

+

set of neighbors

, , ,

2 7 9 14

}

(

(

sketches

2 9

,

14 7

,

(

(

2/&D{%:1F' ' 'V&#':#"')$'=/:3%4'H),6:'8#,'/*%-#'

u3&64#'2/&D{%:1F' 'V&#':#"')$'=/:3%4'H),6:'8#,'I#&",%4'$#%"3,#'

! K&I,#%:#:')=#,4%8''

! r*/I,)D+%-D)$DH),6:s'

! T1#'+3&64#:'/C#C'"1#':#":'%,#'/&6#m#6'+>',#-34%,'*/&D1%:1/&-'


From Min-Hashing to Bundle Min-hashing!

V+:#,=%5)&'PF'7)*+/&/&-'=/:3%4'H),6'4%+#4')$'I#&",%4'$#%"3,#'H/"1':/&-4#'

*/&D1%:1'$,)*'&#/-1+),1))6'H),B:'#=#&'+#E#,'

K*8),"%&"'8%,"'

P (collision) =1− (1 − ovr(I 1 ,I 2 ) k ) n


Bundle Min-hashing!

9

14

7

2

bundling

}

central

visual word

7

set of neighbors

2 9 14

, ,

min-hashing

}

(

(

sketches

7 2

,

7

, 14

(

(

!B#"I1#:')$':/c#'PF'

! J/,:"':B#"I1'I)*8)&#&"F' '9/:3%4'H),6'4%+#4')$'"1#'I#&",%4'$#%"3,#'

! !#I)&6':B#"I1'I)*8)&#&"F' '9/:3%4'H),6'6,%H&'+>'*/&D1%:1'


Bundling Strategy: Radius + Scale band!

u3&64#'6#:I,/+#:'/*%-#',#-/)&'6#8#&6#&"')&4>')&'I#&",%4'$#%"3,#'

Location of central feature

Bundle radius

s max

scale of

central feature

s min

fbI/#&"':#%,I1'$),':8%5%4'&#/-1+),:'+>'3:#')$'%'BD6'",##'


Bundle Min-Hashing!

K&6#m/&-F'

! S#"#,*/&#'I#&",%4'$#%"3,#'%&6'/":':8%5%4'&#/-1+),:'

! 7)*83"#'*/&D1%:1#:'$),':8%5%4'&#/-1+),:'

! u3/46':B#"I1#:'$,)*'=/:3%4'H),6')$'I#&",%4'$#%"3,#'v':/&-4#'*/&D1%:1'

! !"),#':B#"I1#:'/&'1%:1'"%+4#:'

'

t3#,>F'

! t3#,>'1%:1'"%+4#:'$),'/*%-#:'H/"1':%*#':B#"I1#:'%:'a3#,>'/*%-#'

! (#D,%&B',#:34"'4/:"'+>'I):/&#':/*/4%,/">'+#"H##&'$344'+%-D)$DH),6:'1/:")-,%*:C'

K&I,#%:#:'"1#'8#,$),*%&I#.'+#E#,'"1%&',%&B/&-'+>':B#"I1'I)44/:/)&'I)3&"''


Bundle Min-Hashing vs. Bag-of-Words!

fm8#,/*#&":')&'J4/IB,0)-):DRP.':/*/4%,',#:34":')&'Vm$),6'%&6'


Bundle Min-Hashing vs. Bag-of-Words!

fm8#,/*#&":')&'J4/IB,0)-):DRP.':/*/4%,',#:34":')&'Vm$),6'%&6'


Bundle Min-Hashing vs. Bag-of-Words!

fm8#,/*#&":')&'J4/IB,0)-):DRP.':/*/4%,',#:34":')&'Vm$),6'%&6'


Bundle Min-Hashing vs. Bag-of-Words!

fm8#,/*#&":')&'J4/IB,0)-):DRP.':/*/4%,',#:34":')&'Vm$),6'%&6'


Bundle Min-Hashing vs. Bag-of-Words!

fm8#,/*#&":')&'J4/IB,0)-):DRP.':/*/4%,',#:34":')&'Vm$),6'%&6'


Bundle Min-Hashing vs. Bag-of-Words!

fm8#,/*#&":')&'J4/IB,0)-):DRP.':/*/4%,',#:34":')&'Vm$),6'%&6'


Logo Recognition Pipeline: Indexing

Visual Description

Training Images

of each Class

Local

Feature

Extraction

Feature

Aggregation

into

Bundles

Index Bundles

in Hash Tables

Bundle Min-Hashing

V85)&%4F'!>&;


Logo Recognition Pipeline: Testing

Visual Description

nearest-neighbor search

Query /

Test Image

Local

Feature

Extraction

Feature

Aggregation

into

Bundles

Query

Hash

Tables

Re-ranking:

1P-WGC-RANSAC

k-nn

Classification

Bundle Min-Hashing

74%::/XI%5)&F'*%W),/">'=)"#'H/"1'nD&&'I4%::/X#,'

V85)&%4F'!>&tf'$),'t3#,>'fm8%&:/)&'


9/'71#:-)-*;


Recognition Results

Method Precision Recall

Romberg et al. [Romberg11] 0.98 0.61

Revaud et al. [Revaud12] ≥ 0.98 0.73

bag-of-words, 1M 0.991 0.784

bag-of-words, 1M, SP 0.996 0.813

bag-of-words, 1M, SP+SynQE 0.994 0.826

bag-of-words, 1M, SP+SynAUG 0.996 0.825

BmH, 1M, CosSim 0.991 0.803

BmH, 1M, CosSim+SP 0.996 0.818

BmH, 1M, CosSim+SP+SynQE 0.999 0.832

BmH, 1M, CosSim+SP+SynAUG 0.996 0.829

NM

PM

RM

;88,)%I1')3"8#,$),*:']()*+#,-NN`'%&6'](#=%36NP`':/-&/XI%&"4>'

u3&64#'2/&D{%:1/&-':/*/4%,'8#,$),*%&I#'%:'+%-D)$DH),6:C''

u3"F'(#:8)&:#',%5)'NQQm'5*#:':*%44#,'"1%&'+%-D)$DH),6:'L:1)H&'/&'

8,#=/)3:',#:34":M'gh'NQQm'5*#:'$#H#,'/*%-#:'&##6'")'+#'L8):"DM8,)I#::#6'

!>&tf'v'!>&;


Recognition Results

Thank you (see my demo later)!


Questions!


Datasets!

J4/IB,0)-):DRPF' '1E8FGGHHHC*345*#6/%DI)*835&-C6#G/IB,4)-):'

!/c#F ' 'ÄPjQ'/*%-#:'I)&"%/&/&-'4)-):')$'RP'I4%::#:'v'&)&D4)-)'

' '/*%-#:.'",%/&/&-G=%4/6%5)&G"#:"':#"'%&6'8/m#4D4#=#4.' '

' '%&&)"%5)&:'%=%/4%+4#C''

(#",/#=%4F' 'jPÄQ'/*%-#:'/&6#m#6.'wOQ'a3#,/#:'

74%::/XI%5)&F'' 'RwOQ'/*%-#:'/&'"#:"':#"'

Vm$),6F ' '1E8FGGHHHC,)+)":C)mC%IC3BGÅ=--G6%"%G)m+3/46/&-:G'

!/c#F ' 'nQOR'/*%-#:')$'NN'6/p#,#&"')m$),6'+3/46/&-:'

(#",/#=%4F' 'nQOR'/*%-#:'/&6#m#6.'nn'a3#,/#:'


Literature!

]^1/4+/&Q_`' '^1/4+/&.'dC.'713*.'VC.'K:%,6.'2C.'!/=/I.'dC.'e/::#,*%&.';C''

V+W#I"',#",/#=%4'H/"1'4%,-#'=)I%+34%,/#:'%&6'$%:"':8%5%4'*%"I1/&-.'L79^('PQQ_M'

]713*QÄ`' '713*.'VC.'^1/4+/&.'dC.'e/::#,*%&.';C.''

\#%,'6384/I%"#'/*%-#'6#"#I5)&F'*/&D1%:1'%&6'ÇD/6$'H#/-15&-.'Lu297'PQQÄM'

]T),,#:%&/Qw` 'T),,#:%&/.'0C.'!c3**#,.'2C.'z'J/"c-/++)&.';C'''

0#%,&/&-'a3#,>D6#8#&6#&"'8,#X4"#,:'$),':I%4%+4#'/*%-#',#",/#=%4.'L79^('PQQwM'

]0##NQ`' ' 'S%=/6'7C'0##.'t/$%'o#.'%&6'2/I1%#4'K:%,6.''

^%,55)&'2/&D{%:1'$),'^%,5%4'S384/I%"#'K*%-#'S/:I)=#,>.'Lf779'PQNQM'

]()*+#,-NN`' '()*+#,-.'!C.'^3#>).'0C[C.'0/#&1%,".'(C.'=%&'eH)4C'(C.''

!I%4%+4#'0)-)'(#I)-&/5)&'/&'(#%4Di),46'K*%-#:'LK72(NNM'

]()*+#,-NP`' '()*+#,-.'!C.';3-3:".'2C.'(/#:.'7C'UC.'0/#&1%,".'(C.''

()+3:"'J#%"3,#'u3&64/&-.'L^72'PQNPM'

];,%&6W#4)=/INP`';,%&6W#4)=/I.'(C.'e/::#,*%&.';CC''

T1,##'"1/&-:'#=#,>)&#':1)346'B&)H'")'/*8,)=#')+W#I"',#",/#=%4.'L79^('PQNPM'


Image Sources / License!

K*%-#'rJ#%"3,#'u3&64/&-'G;:8%,%-3:sF';3"1),G^1)")-,%81F'f=%&D;*):.'1E8FGG

I)**)&:CH/B/*#6/%C),-GH/B/GJ/4#F;:8%,%-3:Du3&64#CW8-'

L7,#%5=#'7)**)&:';E,/+35)&D!1%,#';4/B#'RCQ'C'

V"1#,:F' 'K*%-#:'I,#%"#6'+>'!"#$%&'()*+#,-'

Y)3'*%>'3:#'"1#:#'X-3,#:'%&6'%6%8"'"1#*'$),':4/6#:.'4#I"3,#:'%&6')"1#,'&)&DI)**#,I/%4'G'

,#:#%,I1D,#4%"#6'83,8):#:'

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