Skip to main content
Erschienen in: Pattern Analysis and Applications 3-4/2008

01.09.2008 | Theoretical Advances

Fusion of textural statistics using a similarity measure: application to texture recognition and segmentation

verfasst von: I. Karoui, R. Fablet, J.-M. Boucher, W. Pieczynski, J.-M. Augustin

Erschienen in: Pattern Analysis and Applications | Ausgabe 3-4/2008

Einloggen

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Features computed as statistics (e.g. histograms) of local filter responses have been reported as the most powerful descriptors for texture classification and segmentation. The selection of the filter banks remains however a crucial issue, as well as determining a relevant combination of these descriptors. To cope with selection and fusion issues, we propose a novel approach relying on the definition of the texture-based similarity measure as a weighted sum of the Kullback–Leibler measures between empirical feature statistics. Within a supervised framework, the weighting factors are estimated according to the maximization of a margin-based criterion. This weighting scheme can also be considered as a filter selection method: texture filter response distributions are ranked according to the associated weighting factors so that the problem of selecting a subset of filters reduces to picking the first features only. An application of this similarity measure to texture recognition is reported. We also investigate its use for texture segmentation within a Bayesian Markov Random Field (MRF)-based framework. Experiments carried out on Brodatz textures and sonar images show that the proposed weighting method improves the classification and the segmentation rates while relying on a parsimonious texture representation.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Randen T (1997) Filter and filter bank design for image texture recognition. Thesis Report, Norwegian University of Science and Technology, Stavanger College Randen T (1997) Filter and filter bank design for image texture recognition. Thesis Report, Norwegian University of Science and Technology, Stavanger College
2.
Zurück zum Zitat Randen T, Husoy J (1999) Filtering for texture classification: a comparative study. IEEE Trans Pattern Anal Mach Intell 21(4):291–310CrossRef Randen T, Husoy J (1999) Filtering for texture classification: a comparative study. IEEE Trans Pattern Anal Mach Intell 21(4):291–310CrossRef
3.
4.
Zurück zum Zitat Ohanian PP, Dubes RC (1992) Performance evaluation for four class of texture features. Pattern Recognit 25(8):819–833CrossRef Ohanian PP, Dubes RC (1992) Performance evaluation for four class of texture features. Pattern Recognit 25(8):819–833CrossRef
5.
Zurück zum Zitat Conners RW, Harlow CA (1980) A theoretical comparison of texture algorithms. IEEE Trans Pattern Anal Mach Intell 2(3):204–222MATH Conners RW, Harlow CA (1980) A theoretical comparison of texture algorithms. IEEE Trans Pattern Anal Mach Intell 2(3):204–222MATH
6.
Zurück zum Zitat Strand J, Taxt T (1994) Local frequency features for texture classification. Int Conf Pattern Recognit 27(10):1397–1406CrossRef Strand J, Taxt T (1994) Local frequency features for texture classification. Int Conf Pattern Recognit 27(10):1397–1406CrossRef
7.
Zurück zum Zitat Sharma M, Singh S (2001) Evaluation of texture methods for image analysis. In: 7th Australian and New Zealand intelligent information systems conference, pp 117–121 Sharma M, Singh S (2001) Evaluation of texture methods for image analysis. In: 7th Australian and New Zealand intelligent information systems conference, pp 117–121
8.
Zurück zum Zitat Singh M, Singh S (2002) Spatial texture analysis: a comparative study. Int Conf Pattern Recognit 1:676–679 Singh M, Singh S (2002) Spatial texture analysis: a comparative study. Int Conf Pattern Recognit 1:676–679
9.
Zurück zum Zitat Clausi A, Deng H (2004) Feature fusion for image texture segmentation. Int Conf Pattern Recognit 1:580–583 Clausi A, Deng H (2004) Feature fusion for image texture segmentation. Int Conf Pattern Recognit 1:580–583
10.
Zurück zum Zitat Haralick R (1979) Statistical and structural approaches to texture. Proc IEEE 67(5):786–804CrossRef Haralick R (1979) Statistical and structural approaches to texture. Proc IEEE 67(5):786–804CrossRef
11.
Zurück zum Zitat Xiuwen L, DeLiang W (2003) Texture classification using spectral histograms. IEEE Trans Image Process 12(6):661–670CrossRef Xiuwen L, DeLiang W (2003) Texture classification using spectral histograms. IEEE Trans Image Process 12(6):661–670CrossRef
12.
Zurück zum Zitat Cula OG, Dana K (2003) 3D texture recognition using bidirectional feature histograms. IEEE Trans Pattern Anal Mach Intell 25(12):1619–1624CrossRef Cula OG, Dana K (2003) 3D texture recognition using bidirectional feature histograms. IEEE Trans Pattern Anal Mach Intell 25(12):1619–1624CrossRef
13.
Zurück zum Zitat Nammalwar P, Ghita O, Whelan PF (2005) Integration of feature distributions for color texture segmentation. Conf Pattern Recognit 1:716–719 Nammalwar P, Ghita O, Whelan PF (2005) Integration of feature distributions for color texture segmentation. Conf Pattern Recognit 1:716–719
14.
Zurück zum Zitat Xu Q, Yang J, Ding S (2004) Texture segmentation using LBP embedded region competition. Electron Lett Comput Vis Image Anal 5(1):41–47 Xu Q, Yang J, Ding S (2004) Texture segmentation using LBP embedded region competition. Electron Lett Comput Vis Image Anal 5(1):41–47
15.
Zurück zum Zitat Fablet R, Bouthemy P (2003) Motion recognition using nonparametric image motion models estimated from temporal and multiscale cooccurrence statistics. IEEE Trans Pattern Anal Mach Intell 25(12):1619–1624CrossRef Fablet R, Bouthemy P (2003) Motion recognition using nonparametric image motion models estimated from temporal and multiscale cooccurrence statistics. IEEE Trans Pattern Anal Mach Intell 25(12):1619–1624CrossRef
16.
Zurück zum Zitat Zhu XLSC, Wu Y (1998) Filters, random fields and maximum entropy (frame): toward a unified theory for texture modeling. Int J Comput Vis 27(2):107–126CrossRefMathSciNet Zhu XLSC, Wu Y (1998) Filters, random fields and maximum entropy (frame): toward a unified theory for texture modeling. Int J Comput Vis 27(2):107–126CrossRefMathSciNet
17.
Zurück zum Zitat Gimel’farb G (1996) Texture modeling by multiple pairwise pixel interactions. IEEE Trans Pattern Anal Mach Intell 18(11):1110–1114CrossRef Gimel’farb G (1996) Texture modeling by multiple pairwise pixel interactions. IEEE Trans Pattern Anal Mach Intell 18(11):1110–1114CrossRef
18.
Zurück zum Zitat Jain A, Farrokhania F (1991) Unsupervised textures segmentation using gabor filters. Pattern Recognit 24(12):1167–1186CrossRef Jain A, Farrokhania F (1991) Unsupervised textures segmentation using gabor filters. Pattern Recognit 24(12):1167–1186CrossRef
19.
Zurück zum Zitat Chang T, kuo C (1993) Texture analysis and classification with tree-structured wavelet transform. IEEE Trans Image Process 2(4):429–441CrossRef Chang T, kuo C (1993) Texture analysis and classification with tree-structured wavelet transform. IEEE Trans Image Process 2(4):429–441CrossRef
20.
Zurück zum Zitat Kullback S (1959) Information theory and statistics. Wiley, New YorkMATH Kullback S (1959) Information theory and statistics. Wiley, New YorkMATH
22.
Zurück zum Zitat Kohavi R, John G (1997) Wrappers for feature subset selection. Artif Intell 97(12):273–324MATHCrossRef Kohavi R, John G (1997) Wrappers for feature subset selection. Artif Intell 97(12):273–324MATHCrossRef
23.
Zurück zum Zitat Marroquin J, Mitter S, Poggio T (1987) Probabilistic solution of ill-posed problems in computational vision. J Am Stat Assoc 82:76–89MATHCrossRef Marroquin J, Mitter S, Poggio T (1987) Probabilistic solution of ill-posed problems in computational vision. J Am Stat Assoc 82:76–89MATHCrossRef
24.
Zurück zum Zitat Geman S, Geman G (1984) Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell 6(6):721–741MATHCrossRef Geman S, Geman G (1984) Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell 6(6):721–741MATHCrossRef
25.
Zurück zum Zitat Besag J (1986) On the statistical analysis of dirty pictures. J R Stat Soc Ser B 48:259–302MATHMathSciNet Besag J (1986) On the statistical analysis of dirty pictures. J R Stat Soc Ser B 48:259–302MATHMathSciNet
26.
Zurück zum Zitat Pieczynski W (1992) Statistical image segmentation. Mach Graph Vis 1(2):261–268 Pieczynski W (1992) Statistical image segmentation. Mach Graph Vis 1(2):261–268
27.
Zurück zum Zitat Duda RO, Hart PE (1973) Pattern classification and scene analysis. Wiley, New YorkMATH Duda RO, Hart PE (1973) Pattern classification and scene analysis. Wiley, New YorkMATH
28.
Zurück zum Zitat Brodatz P (1966) Textures: a photographic album for artists and designers. Dover, New York Brodatz P (1966) Textures: a photographic album for artists and designers. Dover, New York
29.
Zurück zum Zitat Jensen CA, El Sharkawi MA, Marks RJ (2001) Power system security assessment using neural networks: feature selection using Fisher discrimination. IEEE Trans Power Syst 16(4):757–763CrossRef Jensen CA, El Sharkawi MA, Marks RJ (2001) Power system security assessment using neural networks: feature selection using Fisher discrimination. IEEE Trans Power Syst 16(4):757–763CrossRef
30.
Zurück zum Zitat Lianantonakis M, Petillot YR (2005) Sidescan sonar segmentation using active contours and level set methods. In: Conference proceedings OCEANS ’05. 1:719–724 Lianantonakis M, Petillot YR (2005) Sidescan sonar segmentation using active contours and level set methods. In: Conference proceedings OCEANS ’05. 1:719–724
31.
Zurück zum Zitat Ehrhold A, Hamon D, Guillaumont B (2006) The Rebent monitoring network, a spatial integrated acoustic approach to survey nearshore macrobenthic habitats: application to the bay of Concarneau (South Brittany, France). ICES J Mar Sci 63:1604–1615CrossRef Ehrhold A, Hamon D, Guillaumont B (2006) The Rebent monitoring network, a spatial integrated acoustic approach to survey nearshore macrobenthic habitats: application to the bay of Concarneau (South Brittany, France). ICES J Mar Sci 63:1604–1615CrossRef
32.
Zurück zum Zitat Kira K, Rendell L (1992) A practical approach to feature selection. Proc Int Conf Mach Learn 1:249–256 Kira K, Rendell L (1992) A practical approach to feature selection. Proc Int Conf Mach Learn 1:249–256
33.
Zurück zum Zitat Robnik-Sikonja M, Kononenko I (2003) Theoretical and empirical analysis of ReliefF and RReliefF. J Mach Learn Res 53(1-2):23–69MATH Robnik-Sikonja M, Kononenko I (2003) Theoretical and empirical analysis of ReliefF and RReliefF. J Mach Learn Res 53(1-2):23–69MATH
34.
Zurück zum Zitat Gilad-Bachrach R, Navot A, Tishby N (2004) Margin based feature selection—theory and algorithms. ACM Int Conf Proc Ser 69:43 Gilad-Bachrach R, Navot A, Tishby N (2004) Margin based feature selection—theory and algorithms. ACM Int Conf Proc Ser 69:43
Metadaten
Titel
Fusion of textural statistics using a similarity measure: application to texture recognition and segmentation
verfasst von
I. Karoui
R. Fablet
J.-M. Boucher
W. Pieczynski
J.-M. Augustin
Publikationsdatum
01.09.2008
Verlag
Springer-Verlag
Erschienen in
Pattern Analysis and Applications / Ausgabe 3-4/2008
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-008-0108-z

Weitere Artikel der Ausgabe 3-4/2008

Pattern Analysis and Applications 3-4/2008 Zur Ausgabe