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PhD Program in Electronic and Computer Engineering
               PhD School in Information Engineering




                  Neighborhood-Based Feature
                    Weighting for Relevance
                  Feedback in Content-Based
                           Retrieval
                                           Luca Piras
                                    luca.piras@diee.unica.it
                                          Giorgio Giacinto
                                        giacinto@diee.unica.it



    P      R       A
                               Pattern Recognition and Applications Group
                               Department of Electrical and Electronic Engineering
Pattern Recognition and
  Applications Group           University of Cagliari, Italy
          6-05-2009            Neighborhood-Based Feature Weighting - L. Piras   1
Outline

• Relevance Feedback

• Image representation

• Weighted similarity measures

• State of the art: Estimation of Feature Relevance

• Neighborhood-Based Feature Weighting




6-05-2009     Neighborhood-Based Feature Weighting - L. Piras   2
Aim of this work

• Exploiting neighborhood relations to weight
    feature sets

• Weight designed to improve Relevance
    Feedback based on Distance weighted kth-
    Nearest Neighbor

• Dw k-NN estimate the relevance of an image
    according to the (non-)relevant one in its
    nearest neighborhood

6-05-2009          Neighborhood-Based Feature Weighting - L. Piras   3
Distance weighted kth-Nearest
                Neighbor

                              pNN (I )
                               r
                                                                I ! NN nr (I )
relevanceNN (I ) =                               =
                     p   r
                         NN   (I ) + p (I )
                                      nr
                                      NN
                                                     I ! NN r (I ) + I ! NN nr (I )

                                1
where pNN (I ) =
       r                 N
                     (
                   V I ! NN r (I )           )

         (                )
and V I ! NN (I ) " I ! NN (I )




 6-05-2009               Neighborhood-Based Feature Weighting - L. Piras              4
Relevance Feedback

• Query by examples




            User


                                       System
                                       Image
                                      Retrieval


                                      Images
                                     database



6-05-2009            Neighborhood-Based Feature Weighting - L. Piras   5
Relevance Feedback


                                                             k best ranked images




            User


                                       System
                                       Image
                                      Retrieval


                                      Images
                                     database



6-05-2009            Neighborhood-Based Feature Weighting - L. Piras         6
Relevance Feedback


                                                             image labelling




            User


                                       System
                                       Image
                                      Retrieval


                                      Images
                                     database



6-05-2009            Neighborhood-Based Feature Weighting - L. Piras           7
Image representation
                                               I(F)                                           level


                                                                                             image



F=[               f1         …                  fi        …                     fF     ]     feature
              color                          shape                           texture
     f1,1         …           f1,i           fi,1… fi,j               fFi …      representation
col. hist. layout       color moments                       co-occurrence texture
    f1,1,1                  f1,i,1                                           fF,i,1
     .                       .                                                .
     .                       .                                                .
     .                       .                                                .            components
    f1,1,j                  f1,i,j                                           fF,i,j
     .                       .                                                .
     .                       .                                                .
     .                       .                                                .
    f1,1,32                 f1,i,9                                           fF,i,16


      6-05-2009            Neighborhood-Based Feature Weighting - L. Piras                      8
Image representation




color histogram layout

  f1,1 = [g1,1,1, g1,1,2, g1,1,3, g1,1,4 ]
 g1,1,1 = [f1,1,1, …, f1,1,8]
 g1,1,2 = [f1,1,9, …, f1,1,16]
 g1,1,3 = [f1,1,17, …, f1,1,24]
 g1,1,4 = [f1,1,25, …, f1,1,32]
       6-05-2009                  Neighborhood-Based Feature Weighting - L. Piras   9
Image similarity

                                                          1
            #                                         &
  ( )                  (     )      (         )
                 N                                            p
          = % " I A fi, j, k ! I B fi, j, k
                                                  p
S fi, j                                               (           components (lower)
            $ k =1                                    '


S ( fi ) = ! S fi, j
             j
                     ( )                                            representation



S = ! S ( fi )                                                     feature (higher)
      i




 6-05-2009                    Neighborhood-Based Feature Weighting - L. Piras          10
Weighted similarity measures

In order to have good performance into images
retrieval systems
• Relevant images should be considered as
  neighbors each others.
• Non-relevant images should not be in the
  neighborhood of relevant ones.
• Weighted similarity measures.
• Weights related to the capability of feature
  spaces of representing relevant images as
  nearest-neighbors

6-05-2009     Neighborhood-Based Feature Weighting - L. Piras   11
Weighted similarity measures

                                                                   1
            #                                                  &
  ( )                          (        )      (       )
                     N                                                 p
          = % " wi, j, k I A fi, j, k ! I B fi, j, k
                                                           p
S fi, j                                                        (           components (lower)
            $ k =1                                             '


  ( )
                 G
S fi, j = ! wg i d p ( I A , I B )
                   g
                                                                           component subset
             g =1




             j
                         ( )
S ( fi ) = ! wi, j i S fi, j                                                 representation



S = ! wi i S ( fi )
      i
                                                                            feature (higher)




 6-05-2009                         Neighborhood-Based Feature Weighting - L. Piras              12
State of the art

• Inverse of standard deviation
Rui, Huang, Mehrotra. Int. Conf. on Image Processing , 1997


               1
      w fj =                           fj is the j-th feature, !j is its standard deviation
               !j



• Probabilistic learning (PFRL)
Peng, Bhanu, Qing. Computer Vision and Image Understanding, 1999



                e
                 (T ir ( z))
                       fj

      w fj =    F
                                        rfj(z) is the measure of relevance of the j-th
               ! e(   T irl ( z ))
                                             feature for the query z
               l =1




6-05-2009                            Neighborhood-Based Feature Weighting - L. Piras   13
Neighborhood-Based
             Feature Weighting
• “Relevance” of different feature space is
    estimated in terms of their capability of
    representing relevant images as Nearest
    Neighbors

• Relevance of an image is estimated according
    to the relevant and non-relevant images in its
    nearest nieghborhood



6-05-2009       Neighborhood-Based Feature Weighting - L. Piras   14
Neighborhood-Based
                  Feature Weighting

                                                                          " d (I ,N )
                                                                                fx


       wf =
                             p   r
                                 NN   (f )
                                       x
                                                        =                i !R
                                                                                min    i

            x
                p   r
                    NN   (f ) + p (f ) "d
                             x
                                           nr
                                           NN       x
                                                                   fx
                                                                   min   (I ,R) + " d (I ,N )
                                                                          i
                                                                                             fx
                                                                                             min   i
                                                            i !R                      i !R


                                           1
    where p     r
                     (f ) = V
                NN       x             r
                                       NN    (f )
                                                x




                                    1
    and VNN ( fx ) !                     #    d min (I i ,R )
         r                                      fx

                                 card(R) i "R



6-05-2009                        Neighborhood-Based Feature Weighting - L. Piras                       15
Neighborhood-Based
             Feature Weighting
• Evaluation of capability to exploit neighborhood
    relations in terms of weighted similarity measures
    and in terms of weighted relevance score :
     – Components level

     – Component subset level



                  ( )
                S fi, j                      relevanceNN fi, j    ( )


6-05-2009       Neighborhood-Based Feature Weighting - L. Piras         16
Why better?


• Inverse of standard deviation
     – Doesn’t use information about neighborhood of
            relevant images

• Probabilistic learning (PFRL)
     – It considers only relevant images




6-05-2009            Neighborhood-Based Feature Weighting - L. Piras   17
Dataset

• Corel 19511 images
• 43 classes            (min: 96 - max: 1544 images)




6-05-2009      Neighborhood-Based Feature Weighting - L. Piras   18
Feature sets

• 4 feature sets
     – Co-Occurrence Texture                           (4x4 subsets)
            • 4 directions x 4 values

     – Color Moments                                   (3x3 subsets)
            • first 3 moments x (H, S, V)

     – Color Histogram                                 (4x8 subsets)
            • 8 ranges of H x 4 ranges of S

     – Color Histogram Layout                          (4x8 subsets)
            • 4 sub-images x 8 color




6-05-2009              Neighborhood-Based Feature Weighting - L. Piras   19
Experiment setup


• 500 queries
• 9 iterations
• 20 images retrieved each iteration




6-05-2009        Neighborhood-Based Feature Weighting - L. Piras   20
Legend

                Dw 2-NN no weight



        *       SVM no weight

                Dw 2-NN Probabilistic learning

                Dw 2-NN Inverse of standard deviation

                Dw 2-NN Neighborhood-Based

                Dw 2-NN N-Based component subset

                Dw 2-NN N-Based Score component subset

6-05-2009   Neighborhood-Based Feature Weighting - L. Piras   21
Experimental Results
                        Color Histogram




6-05-2009     Neighborhood-Based Feature Weighting - L. Piras   22
Experimental Results
                                  Color Histogram




                    1
   F=
                1         1
                    +
            2 ! prec 2 ! recall




6-05-2009               Neighborhood-Based Feature Weighting - L. Piras   23
Experimental Results
                  Color Histogram (PFRL)




6-05-2009     Neighborhood-Based Feature Weighting - L. Piras   24
Conclusions

• Reported results show that a weighted measure
    improve the performance of the NN technique

• Weighted distance metric based on feature
    subset provided the best results

• Neighborhood-Based weights provide similar or
    better results with respect to PFRL but without
    annoying tuning operations



6-05-2009       Neighborhood-Based Feature Weighting - L. Piras   25

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Wiamis2009 Pres

  • 1. PhD Program in Electronic and Computer Engineering PhD School in Information Engineering Neighborhood-Based Feature Weighting for Relevance Feedback in Content-Based Retrieval Luca Piras luca.piras@diee.unica.it Giorgio Giacinto giacinto@diee.unica.it P R A Pattern Recognition and Applications Group Department of Electrical and Electronic Engineering Pattern Recognition and Applications Group University of Cagliari, Italy 6-05-2009 Neighborhood-Based Feature Weighting - L. Piras 1
  • 2. Outline • Relevance Feedback • Image representation • Weighted similarity measures • State of the art: Estimation of Feature Relevance • Neighborhood-Based Feature Weighting 6-05-2009 Neighborhood-Based Feature Weighting - L. Piras 2
  • 3. Aim of this work • Exploiting neighborhood relations to weight feature sets • Weight designed to improve Relevance Feedback based on Distance weighted kth- Nearest Neighbor • Dw k-NN estimate the relevance of an image according to the (non-)relevant one in its nearest neighborhood 6-05-2009 Neighborhood-Based Feature Weighting - L. Piras 3
  • 4. Distance weighted kth-Nearest Neighbor pNN (I ) r I ! NN nr (I ) relevanceNN (I ) = = p r NN (I ) + p (I ) nr NN I ! NN r (I ) + I ! NN nr (I ) 1 where pNN (I ) = r N ( V I ! NN r (I ) ) ( ) and V I ! NN (I ) " I ! NN (I ) 6-05-2009 Neighborhood-Based Feature Weighting - L. Piras 4
  • 5. Relevance Feedback • Query by examples User System Image Retrieval Images database 6-05-2009 Neighborhood-Based Feature Weighting - L. Piras 5
  • 6. Relevance Feedback k best ranked images User System Image Retrieval Images database 6-05-2009 Neighborhood-Based Feature Weighting - L. Piras 6
  • 7. Relevance Feedback image labelling User System Image Retrieval Images database 6-05-2009 Neighborhood-Based Feature Weighting - L. Piras 7
  • 8. Image representation I(F) level image F=[ f1 … fi … fF ] feature color shape texture f1,1 … f1,i fi,1… fi,j fFi … representation col. hist. layout color moments co-occurrence texture f1,1,1 f1,i,1 fF,i,1 . . . . . . . . . components f1,1,j f1,i,j fF,i,j . . . . . . . . . f1,1,32 f1,i,9 fF,i,16 6-05-2009 Neighborhood-Based Feature Weighting - L. Piras 8
  • 9. Image representation color histogram layout f1,1 = [g1,1,1, g1,1,2, g1,1,3, g1,1,4 ] g1,1,1 = [f1,1,1, …, f1,1,8] g1,1,2 = [f1,1,9, …, f1,1,16] g1,1,3 = [f1,1,17, …, f1,1,24] g1,1,4 = [f1,1,25, …, f1,1,32] 6-05-2009 Neighborhood-Based Feature Weighting - L. Piras 9
  • 10. Image similarity 1 # & ( ) ( ) ( ) N p = % " I A fi, j, k ! I B fi, j, k p S fi, j ( components (lower) $ k =1 ' S ( fi ) = ! S fi, j j ( ) representation S = ! S ( fi ) feature (higher) i 6-05-2009 Neighborhood-Based Feature Weighting - L. Piras 10
  • 11. Weighted similarity measures In order to have good performance into images retrieval systems • Relevant images should be considered as neighbors each others. • Non-relevant images should not be in the neighborhood of relevant ones. • Weighted similarity measures. • Weights related to the capability of feature spaces of representing relevant images as nearest-neighbors 6-05-2009 Neighborhood-Based Feature Weighting - L. Piras 11
  • 12. Weighted similarity measures 1 # & ( ) ( ) ( ) N p = % " wi, j, k I A fi, j, k ! I B fi, j, k p S fi, j ( components (lower) $ k =1 ' ( ) G S fi, j = ! wg i d p ( I A , I B ) g component subset g =1 j ( ) S ( fi ) = ! wi, j i S fi, j representation S = ! wi i S ( fi ) i feature (higher) 6-05-2009 Neighborhood-Based Feature Weighting - L. Piras 12
  • 13. State of the art • Inverse of standard deviation Rui, Huang, Mehrotra. Int. Conf. on Image Processing , 1997 1 w fj = fj is the j-th feature, !j is its standard deviation !j • Probabilistic learning (PFRL) Peng, Bhanu, Qing. Computer Vision and Image Understanding, 1999 e (T ir ( z)) fj w fj = F rfj(z) is the measure of relevance of the j-th ! e( T irl ( z )) feature for the query z l =1 6-05-2009 Neighborhood-Based Feature Weighting - L. Piras 13
  • 14. Neighborhood-Based Feature Weighting • “Relevance” of different feature space is estimated in terms of their capability of representing relevant images as Nearest Neighbors • Relevance of an image is estimated according to the relevant and non-relevant images in its nearest nieghborhood 6-05-2009 Neighborhood-Based Feature Weighting - L. Piras 14
  • 15. Neighborhood-Based Feature Weighting " d (I ,N ) fx wf = p r NN (f ) x = i !R min i x p r NN (f ) + p (f ) "d x nr NN x fx min (I ,R) + " d (I ,N ) i fx min i i !R i !R 1 where p r (f ) = V NN x r NN (f ) x 1 and VNN ( fx ) ! # d min (I i ,R ) r fx card(R) i "R 6-05-2009 Neighborhood-Based Feature Weighting - L. Piras 15
  • 16. Neighborhood-Based Feature Weighting • Evaluation of capability to exploit neighborhood relations in terms of weighted similarity measures and in terms of weighted relevance score : – Components level – Component subset level ( ) S fi, j relevanceNN fi, j ( ) 6-05-2009 Neighborhood-Based Feature Weighting - L. Piras 16
  • 17. Why better? • Inverse of standard deviation – Doesn’t use information about neighborhood of relevant images • Probabilistic learning (PFRL) – It considers only relevant images 6-05-2009 Neighborhood-Based Feature Weighting - L. Piras 17
  • 18. Dataset • Corel 19511 images • 43 classes (min: 96 - max: 1544 images) 6-05-2009 Neighborhood-Based Feature Weighting - L. Piras 18
  • 19. Feature sets • 4 feature sets – Co-Occurrence Texture (4x4 subsets) • 4 directions x 4 values – Color Moments (3x3 subsets) • first 3 moments x (H, S, V) – Color Histogram (4x8 subsets) • 8 ranges of H x 4 ranges of S – Color Histogram Layout (4x8 subsets) • 4 sub-images x 8 color 6-05-2009 Neighborhood-Based Feature Weighting - L. Piras 19
  • 20. Experiment setup • 500 queries • 9 iterations • 20 images retrieved each iteration 6-05-2009 Neighborhood-Based Feature Weighting - L. Piras 20
  • 21. Legend Dw 2-NN no weight * SVM no weight Dw 2-NN Probabilistic learning Dw 2-NN Inverse of standard deviation Dw 2-NN Neighborhood-Based Dw 2-NN N-Based component subset Dw 2-NN N-Based Score component subset 6-05-2009 Neighborhood-Based Feature Weighting - L. Piras 21
  • 22. Experimental Results Color Histogram 6-05-2009 Neighborhood-Based Feature Weighting - L. Piras 22
  • 23. Experimental Results Color Histogram 1 F= 1 1 + 2 ! prec 2 ! recall 6-05-2009 Neighborhood-Based Feature Weighting - L. Piras 23
  • 24. Experimental Results Color Histogram (PFRL) 6-05-2009 Neighborhood-Based Feature Weighting - L. Piras 24
  • 25. Conclusions • Reported results show that a weighted measure improve the performance of the NN technique • Weighted distance metric based on feature subset provided the best results • Neighborhood-Based weights provide similar or better results with respect to PFRL but without annoying tuning operations 6-05-2009 Neighborhood-Based Feature Weighting - L. Piras 25