Image Retrieval with Saliency Object Weighted and Bag of Visual Pair

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r o h t u a g n i d n o p s e rr o C * : s d r o w y e

K ImageRet ireval, FeatureFusion, SailencyObject ,ColorI nformaiton.

.t c a r t s b

A Inmode lofi mage-retireva lbasedonbag- fo -features ,al argenumbe rofi nformaiton sil os t h g i h e h t g n i z it n a u q f o s s e c o r p e h t g n ir u

d -dimen isona lSIFTf eature sa svsiua lwords. tIl eadst o the g n i m e e

t o ferror smatchingf eaturepoin st .Toaddresst hi sproblem, t hi spape rcouplest hecolori n -o

f rmaitoni ntot heB oFmode la sacomplementt o is tff eature ,st wodifferentf eature sareexrtactedt o h ti w r i a p r o t p ir c s e d e h t g n i z it n a u q y b d e n i a t b o s i ri a p l a u si v a d n a n o i g e r ts e r e t n i e m a s e h t t n e s e r p e r d n e p e d n i o w

t en tcodebook .sWha’t smore ,weweigh tsimliartiydegreeofi mages byu isngt hesal-i e h t e s a e r c e d o t e g a m i f o a e r a y c n

e semanitcgap between l ow-levelf eatures andhigh-leve lexpres -n

o

is s .Wes howt hats uchanapproachcan isgniifcanltyoutpefrormmatchingr es s utl compared wtih g a b f o l e d o m l a n o it i d a

rt - fo -feature sonHo ildaydataset .Fu trhermore ,ours rtategyo fweighitngbased y c n e il a

s fu trher improved ro u pefrormancei ni mage-retireva.l

Introduciton

f o y t n e l p , s r a e y r o

F conten tbased image retireva lmethods, such a s[1 ,2] ,have dominated al l e g a m i f o l e d o M . n o is i v r e t u p m o c f o s n i a m o

d -ret ireva lbased on bag- fo -feature shave pretty good . d o h t e m e s e h t f o l l a g n o m a e c n a m r o fr e

p InB oFmodel ,af eaturedetector[ 3]i semployedt oex rtac t s n o i g e r l a c o l t n e il a s e h

t inani mage,t heneachr egion ssir epresented a sahigh-dimen isonalf eature s e r u t a e f T F I S . g . e ( r o t c e

v [3 ]o r ti svairant s[4 ,5] )by u isng fo loca lfeature sdescirpto r[3 ,6] .A o c n e h t si k o o b e d o

c nsrtuctedo ffilnebyunsuperv siedclu tseirng,t ypicallyak-mean salgortihm. The c o v l a u si v a s a o t d e rr e f e r y ll a u s u s i k o o b e d o c g n it l u s e

r abulary,a ndt hec enrtoidsa sv siua lword .sThe

v n

I erseDocumen tFrequency I(DF)[ 3 ,7]i sprevalenltyuitilzedi nB oFmodelt oweigh teachv siua l e h T . y c n e u q e rf n o d e s a b s d r o

w FB o representaitoni sobtainedbyquanitzingt hel oca ldescirptorsi nto ll a n i F . s d r o w l a u si v f o m a r g o ts i h y c n e u q e rf a n i g n it l u s e r , y r a l u b a c o v l a u si

v y ,vairou sindexing

s d o h t e

m 8[ ]arebuitlf orf a tsi magesr anking. e v it a n i m ir c si d e h t e v o r p m i o t r e d r o n

I powe ro fSIFTvsiua lwords ,andt hel oca lcolori nformaiton e h t o t n i d e c u d o rt n i

si B oFmode.lThi spape remployst woi ndependen tcodebookst o ifnsihf eature s h c i h w , s d r o w l a u si v o w t s a d e d o c n e s i s n o i g e r t n e il a s h c a E . n o it a z it n a u

q si calleda sv siua lpai.r

i y ll a u s u e r a s e r u t a e f y si o n , e l a c s e g a m i e ri t n e n i d e h si n if s i n o it c a rt x e e r u t a e f e h t ,l a r e n e g n

I n

-lf n i h c i h w n o it a m r o f n i d n u o r g k c a b r o f h c r a e s e g a m i n i st c e ff e e v it a g e n e s u a c y a m s i h T . d e v l o

v uence s

o i v b o e g a m i f o n o it a t n e s e r p e r e h

t u lsy .Vsiua latteniton model[ 12 ]wa sproposed by r esearcherst o -t a e v it c e l e s c it a m o t u a g n it a l u m is h g u o r h t e n e c s f o s n o i g e r t n e il a s s e t a c o l tI . m e l b o r p s i h t e t a i v e ll a m n o it n e

t echansimo fhumans .Sof a,rs ailencymode lhavearleadybeenusedi nmany ifeld ,ss ucha s g n i d o c e s r a p

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m si n a h c e m n o it n e tt a l a u si v h ti w h c r a e s e g a m i g n i n i b m o c s i r e p a p s i h t f o n o it u b ir t n o c r e h t o n

A .

2 1 [ l e d o m n o it n e tt a l a u si v e h

T ]i susedt ogeta s ailen tobject .Thent hevsiua lparisa ree x rtactedi nt he i

g e r t c e j b o t n e il a

s ont oweighitngt he ismliartiymatching .Ani mageappear sailkei nbothgloba land e

r o c s r a li m is h g i h a d e n g is s a e b l li w s l e v e l l a c o

l .

. sl i a t e d n i d o h t e m r u o e c u d o rt n i e w , 2 n o it c e S n I . s w o ll o f s a d e z i n a g r o s i r e p a p e h t f o t s e r e h T

d n a s tl u s e r l a t n e m ir e p x

E dsicus ison arepresented i n Seciton 3 .Finally,t heconclu ison i sdrawn i n .

4 n o it c e S

d o h t e M

g a b d e s o p o r p e h t f o n o it p ir c s e d l a m r o f a s e v i g n o it c e s si h

T - fo -vsiual-pai r rfamework .Thi spape r

n i n o is u f e r u t a e f e h t s r e d is n o

c B oF model .Thecoupled COLOR andSIFTfeature sareex rtacted t o 1 . g i F s A . s e c a p s e r u t a e f h t o b n i d e t a l u c l a c si e r o c s r a li m is e h t , g n i h c t a m e r u t a e f n I .s e g a m i e h t t c i p e d

d e l p u o c e h t fi , d r o w l a u si v T F I S e m a s e h t o t d e z it n a u q s t n i o p y e k o w t n e v i g , s w o h

s COLORfeature s

a m y e h t ,t n e r e ff i d e r

a ybecon isderedt obeaf aslematch.

g i

F u 1. re Exampleo fWrongMatchingFeaturePoint.

e r u t a e

F Extrac itonandQuan itifca iton

e l a c

S -invairan tkeypoint sare detected wtih detecto r[3] .Then ,a sfi tdescirpto rand a colo r e

h t ,r e p a p s i h t n I .t n i o p y e k h c a e d n u o r a n o i g e r l a c it p il l e e h t m o rf d e t c a rt x e e r a r o t p ir c s e

d COLOR

4 6 a s i e r u t a e

f -dimen isona lHSV colo rhi tsogram obtained by u isng 16×4×1bin sfo rH, S ,V .

st n e n o p m o

c The descirpto rpari sneed to be quanitzed into vsiua lword sfo rfas tmatching. A d

e s u y a w n o m m o

c in many mutl ifeature smode l[10 ] si t o concatenate descirptors i nto one high a

z it n a u q r o f k o o b e d o c e l g n is a e s u d n a , r o t p ir c s e d l a n o is n e m i

d iton . However , the ismple

t n e r e ff i d n i g n it t e g e r a s e r u t a e f e h t e c n is e l b a n o s a e r n u s i n o it a n e t a c n o

c features paces .Moitvatedby

1 1

[ ] ,we use two independen tcodebook s to ifnsih feature quanitzaiton .Suppo isng given an a

e r a g n it s e r e t n

i ,we can ex rtac tCOLOR and SIFT feature t o obtain apai ro ffeaturedescirpto r r

o t p ir c s e d e r u t a e f e z it n a u q e w n e h t d n A

. by u isng driectory and

g n it a r e n e g

, avsiua lpai r .Thel ength oft heset wocodebookare and e

v it c e p s e

r ly .Theproces scanbes eeni nFig . 2.

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IndexingandRetrieval

x e d n i d e tr e v n i e h

T isgni ifcan lty promote sthe efifciency o fB oF based image retireval .The n

o

c venitonali nvetred i ndex i sbuli tonlyu isng onekindoff eature,t ypicallyt he istff eature.I nt hi s ,

r e p a

p ass howni nFig .3 ,webu lida2-Di nvetred i ndext oachievef eaturef u isonati ndexingl evel . e

h

T SIFT feature i sno tthe only itcke tfo rinvetred index ,and the loca lCOLOR feature i s 2

e h t a i v r e w o p e v it a n i m ir c si d l a n o it i d d a e d i v o r p o t d e t a r o p r o c n

i -Di nvetredi ndex .Then,t hei mage

d e h c t a m s ti n o s e t o v e g a m i y r e u q e h t n i ri a p l a u si v h c a E . m e l b o r p g n it o v a s a d e t a l u m r o f si g n i h c r a e s

e h t m o rf s e g a m

i invetredi ndex.

g i

F u 3. re Frameworko fMulitpleFeatureFu isoni nImageRetireval.

Giving two v siua lpari s and ,they are matched only i fthei r e

n if e d s i n o it c n u f h c t a m e h t ,l a c it n e d i e r a d r o w l a u si v r o l o c d n a t fi s g n i d n o p s e rr o

c d :sa

( 1)

l a n o it n e v n o c e h t s a e m a

S B oFmode,lt heI nverseDocumen tFrequency I(DF)s chemei sappiledt o .

ri a p l a u si v e h t t h g i e

w TheI DFvalueo fvsiua lpai r i sdeifnedas:

( 2)

n

I E 2q . ,Ni snumbe rofi magesi ndataset da n si thenumbe ro fv siua lpai r .t

e s a t a d n

i Moreover ,the normailzaitoni sasloadoptedi nt he2-Di nvetredi ndex.Givenani mage ,thet erm rfequency( TF )o feach vsiua lparii scalculated a sav siua lpai rhi tsogram ,

, , i sword rfequencyo fv siua lpai r in

e g a m

i .The normailzef uncitoni sdeifneda :s

( 3)

ir a li m is e h t , e r o f e r e h

T tys coreoft woi mage and si deifnedas:

( 4)

t n e il a

S Object sWeighted

r a li m is d e s a b t c e j b o r a l u c it r a p f o k s a t e h t s r e d is n o c r e p a p s i h

T imagesearch.I ni magesearching

e g a m i e ri t n e n i d e t c a rt x e e r a s r o t p ir c s e d , s s e c o r

(4)

o it a t n e s e r p e r e h

t n o fimages ,are aslo involved .Thi swli lcompromsie sthe searching accuracy o

n r o t p ir c s e d e h t f o e s u a c e

b sie .To address t hi sproblem, t he vsiua latteniton mechansim [12 ]i s .s

n o i g e r e v it a m r o f n i ts o m e h t t c a rt x e o t d e il p p a

e g a m i n a n e v i

G I ,wef ris tcalculates ailencymapSalu isngt hevsiualattenitonmode lproposedi n 9

[ ,]e achpixesli nSalshowst hes ailencyvalueoft hes amel ocaitoni ni mage I .Thent hes ailen tobjec t d

e n i a t b o s

i accordingt ot hemethod[ 13] .Wer e iszet heelilpitcalr egionaroundeachs fi tkeypoint sa s i

g n i y fi t n e d i y b d e d n u o f si t c e j b o t n e il a S . n o it a l u c l a c t s a f r o f n o i g e r r a l u g n a t c e r

a mager egionst ha t

e ri t n e e h t f o e u l a v y c n e il a s n a e m e h t e c i w t e b o t t e s s i t a h t d l o h s e r h t a e v o b a y c n e il a s e g a r e v a e v a h

n o i g e r f o e u l a v y c n e il a s e g a r e v a e h T . e g a m

i andt hemeans ailencyvalueofi mage Iaredeifneda :s

( 5)

( 6)

I n Eq .5 and Eq .6, and , and are the width and heigh to fregion and image I , e

p s e

r citvely.A tfe rconfrimingsailencyobject ,wewouldge tvi ison-pai rhi tsogramviai stf eatureand s

e g a m i g n o m a e r o c s y ti r a li m is e h T . n o it a z it n a u q f o s s e c o r

p canbecalculatedandbe

: e r o c s y ti r a li m is e t a m it l u m ri f n o c o t t h g i e w r a e n il o t d e s u

da n subjectt o

( 7)

t n e m i r e p x E

l p p a e

W i ed ou rmode lto Hoildays[14] dataset .In the following, we repor tou rresutl sand n

i g ir o f o t l u s e r e h t h ti w s n o si r a p m o

c a lB oFmode.l

t e s a t a D

s y a d il o

H .Thedatase tcon is ts so f1491vacaiton photograph sco rresponding t o 500group sbased s a n e e s e b n a c s tl u s e r e h T . y r e u q a s a s e v r e s s s a l c h c a e m o rf e g a m i ts ri F . t c e j b o r o e n e c s e m a s n o

d e ir e u q e h t h t o b t a h t s a g n o l s a e v it is o

p imageandqueryi magebelongt ot hes ameclass .mAP(mean .

y c a r u c c a g n i h c r a e s e h t e r u s a e m o t d e y o l p m e s i ) n o is i c e r p e g a r e v a

f o s s e c o r

P Experiment e

n il e s a

B . Thi spape ruseo irgina lB oF mode la sbenchmark expeirmen tand desc irbei magevia T

F I

S feature .Wee mploymethodi n[ 14]t odetecta nddescirbeSIFTf eaturepoinst .Thedimen isono f k

o o b e d o

c ,beingusedi nproces so fSIFTf eaturequanitzaiton ,sis ett o20K .Thecodebooki sobtained .

K 0 6 r k c il F n i g n ir e ts u l c a i v

r o l o

C Codebook.In ordert ol earnacolo rcodebook t hati smoreadaptedt onaturali mage ,st hi s :

k o o b e d o c r o l o c e h t g n i c u d o r p r o f e r u d e c o r p g n i w o ll o f e h t s e s u r e p a p

)

1 Selec t10000r andomi mage s rfomFilck .r )

2 Re iszeeachi maget o256×256pixesl ,conver t tit oHSVcolors paceands pil ttii n block so f 16×16pixesl ,256blocksi nt ota.l

)

3 Ex rtac ta64-dimen isona lcolo rhi tsogramf o reachblock .Then ,as e to f64×2560000COLOR k

a g n is u d e r e ts u l c s i s e r u t a e

f -mean salgortihm ,producingacolo rcodebookwhich iszei sn.

n o it a z i m it p

O .Wese t iszeo fcodebookt o100i nCS_BoF ,Hammingdi tsancet hresholdto30and r

e t e m a r a p g n it h g i e

w to 7i nCS_BoF_HEc_,_s_o_f_t_q_u_a_n_it _if_c_a_it_o_n _f_a_c_t_o_r_s_t_o ₇₀_i_n _C_S_{_}_B_o_F_{_}_H_Ec_{_}_M_Ac_.

e r a e v o b a r e t m a r a p f o l l

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tl u s e

R .Wec onduc toure xpeirmensti nHoilday sdatasetbyu isnga l lo fou rmodel .Fris timage rfom h

c a

e classservesa saqueryandquery500 itme speri mage .Ther esul tcanbes eeni nTable2. T ba l 2. e ImageRet ireva lPefrormanceonHo ilday.

Method m AP(%)

F o

B 46.70

F o B _ S

C 59.02

_ F o B _ S

C HEc ₆₂_.₃₂

_ F o B _ S

C EH c_{_}_M_Ac ₆₃_.₆₁

. % 0 7 . 6 4 t u o b a si t e s a t a d n i l e d o m F o B l a n o it i d a rt g n is u y b t l u s e r l a v e ir t e r e h t , 2 e l b a T n i n w o h s s A

h t o b g n is u f y b d o h t e m l a v e ir t e r a e s o p o r p d n a l e d o m F o B o t n i n o it a m r o f n i r o l o c e h t d d a r e p a p s i h T

d n a T F I

S COLOR feature which retireva lresul ti s59.02% ,increased 12.32% compared to BoF r

o f y ti r a li m is t h g i e w o t e c n a ts i d g n i m m a H e s u e w , e m it e m a s e h t t A . l e d o

m COLORfeatureanduse

e l g n is y ti t n a u q o t y g e t a rt s e v it a ti t n a u q t f o

s COLOR feature to mulitple colo rwords ,obtaining

S

C _BoF_HEc _m_o_d_e_l_C_S_{_}_B_o_F_{_}_H_Ec_{_}_M_Ac _m_o_d_e_l_._T_h_e_i_r_r_e_t_ir_e_v_a_l_r_e_s_u_tl _a_r_e ₆₂_.₃₂_% _a_n_d ₆₃_._{6 ,}₁_%

. y c a r u c c a l a v e ir t e r e h t g n i v o r p m i r e h tr u f

si h t n

I pape,rt hecompaitblitiyo fourmodeli saslobeenexpeirmented .Wecombinet heCS_BoF l

e d o

m ,CS_BoF_HEc_m_o_d_e_l_,_C_S_{_}_B_o_F_{_}_H_Ec_{_ A}_M c_m_o_d_e_l_a_n_d_b_u_r_s_it_n_e_s _s_w_e_i_g_h _it_n_g_[₁_{5] , g}_{a -}_v _I_D_F_[₁_{6] ,}

E H ( g n i d d e b m E g n i m m a

H s _{[) ] ,}₁₄ _M_u_l_it_p_l_e _A_s _is_g_n_m_e_n_t_{( A}_M s _{) [ 7}₁ _]_t_o_g_e_t_h_e_r_,_a_n_d _v_e_r_fi_y _t_h_e_m _i_n

f o n o is n e m i d e h t h c i h w n i T F I S f o s is a b e h t n o y l n o m h ti r o g l a d e v o r p m i e s e h T . t e s a t a d y a d il o H

E H f o e d o c g n i m m a

H s_si₁₂₈_a_n_d_t_h_e_s_o_f_t_q_u_a_n_it_if_c_a_it_o_n_f_a_c_t_o_r _s_o _f_M_A s_i _s₃_._T_h_e_r_e_s_u_l_t_c_a_n_b_e_s_e_e_n_i_n

. 3 e l b a T

s u o u n it n o c e k a m m h ti r o g l a d e v o r p m I . e l b it a p m o c e r a r e p a p r u o f o s l e d o m , 3 e l b a T n I

v o r p m

i emen t in retireva l pefrormance . Therefore , exsiitng improved algortihm fo r SIFT are .

d o h t e m r u o r o f e l b it a p m o c

T ba l 3. e Resul to fCompaitblitiyExpeirmen st.

Method a - FveI D _B_u_r _ts H Es M_sA WBeeigfohrtee d

P A m

A rtf e Weighte d

P A m

F o B _ S

C √ 58.93 59.90

F o B _ S

C √ 59.88 60.54

F o B _ S

C √ √ 60.62 60.83

E H _ F o B _ S

C c _√ ₇₂_.₃₂ ₇₂_.₄₉

_ S

C BoF_HEc _√ _√ ₇₂_.₆₄ ₇₂_.₉₁

E H _ F o B _ S

C c _√ _√ ₇₃_.₀₉ ₇₃_.₀₇

E H _ F o B _ S

C c _√ _√ _√ ₇₃_.₁₃ ₇₃_.₂₅

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Ac √ √ 82.72 83.01

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C c_{_}_M

Ac √ √ √ 82.98 83.33

E H _ F o B _ S

C c_{_}_M

Ac √ √ √ 83.23 83.73

E H _ F o B _ S

C c_{_}_M

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