Using Color and Local Binary Patterns for Texture Retrieval


  • Michela Lecca Fondazione Bruno Kessler, ICT - Technologies of Vision, Trento, Italy



Image Retrieval, Color, Texture, Local Binary Patterns


Texture plays a crucial role to detect and recognize materials in real-world pictures. The choice of the visual descriptors that are the most appropriate for the detection and recognition tasks is generally a hard issue, due to the wide range of circumstances under which the imaged objects can appear. This work addresses the problem of the illuminant invariant texture retrieval, and it proposes two algorithms that combine illuminant invariant color information with the textural cues output by the local binary pattern (LBP) operator. The experiments, carried out on a public database, show that the joint use of color and texture features remarkably improves the retrieval performance of techniques based on LBP only.

Author Biography

Michela Lecca, Fondazione Bruno Kessler, ICT - Technologies of Vision, Trento, Italy

Michela Lecca is a permanent researcher of the Research Unit Technologies of Vision of Fondazione Bruno Kessler (Trento, Italy). Her research interests include color image processing, object recognition, image retrieval and labeling, and low-level image processing for embedded vision systems. She is a member of the International Association for Pattern Recognition IAPR-GIRPR and of the Gruppo Italiano del Colore-Associazione Italiana Colore.


R. Veltkamp, H. Burkhardt and H.-P. Kriegel, (eds), State-of-the-art in content-based image and video retrieval, Springer Science & Business Media, 2013.

M. Lecca, “Color improves Texture Retreval,” in Proc. of X Colour Conference, Genova, Italy, 2014.

M. Pietikäinen, T. Ojala and T. Mäenpää, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, 2002.

J. Y. Choi, K. Plataniotis and Y.-M. Ro, “Using colour local binary pattern features for face recognition,” in Proc. of 17th IEEE International Conference on Image Processing, 2010.

G. Anbarjafari, “Face recognition using color local binary pattern from mutually independent color channels,” EURASIP Journal on Image and Video Processing, vol. 1, no. 6, 2013.

S. Banerji, A. Verma and C. Liu, “LBP and Color Descriptors for Image Classification,” in Cross Disciplinary Biometric Systems, Springer, 2012, pp. 205-225.

C. Zhu, C.-E. Bichot and I. Chen, “Multi-scale Color Local Binary Patterns for Visual Object Classes Recognition,” in Proc. of 20th International Conference on Image Processing, 2010.

G. Xue, J. Sun and L. Song, “Dynamic background subtraction based on spatial extended center-symmetric local binary pattern,” in IEEE International Conference on Multimedia and Expo, 2010.

D. Huang, S. Caifeng, M. Ardabilian, Y. Wang and C. Liming, “Local Binary Patterns and Its Application to Facial Image Analysis: A Survey,” Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 41, no. 6, pp. pp.765-781, Nov. 2011.

S. Malathi and C. Meena, “An efficient method for partial fingerprint recognition based on local binary pattern,” in IEEE International Conference on Communication Control and Computing Technologies, 2010.

L. Nanni, A. Lumini and S. Brahnam, “Survey on LBP based texture descriptors for image classification,” Expert Systems with Applications, vol. 39, no. 32, 2012.

G. Schaefer, “Colour for Image Retrieval and Image Browsing,” in Proc. of ELMAR, 2011.

E. Schechter, Handbook of Analysis and its Foundations, London: Academic Press Inc, 1997.

G. Finlayson, M. Drew and B. Funt, “Diagonal transforms suffice for color constancy,” in Proc. of Fourth International Conference on Computer Vision, 1993.

M. Lecca and S. Messelodi, “Illuminant Change Estimation via Minimization of Color Histogram Divergence,” in Computational Color Imaging Workshop , 2009.

M. Lecca, “On the von Kries Model: Estimation, Dependence on Light and Device, and Applications,” in Advances in Low-Level Color Image Processing, Springer, 2014, pp. 95-135.

T. Ojala, T. Mäenpää, M. Pietikäinen, H. Viertola, J. Kyllönen and S. Huovinen, “Outex - New framework for empirical evaluation of texture analysis algorithms,” in Proc. of 16th International Conference on Image Processing, 2002.

D. Harwood, T. Ojala, M. Pietikäinen, S. Kelman and L. Davis, “Texture classification by center-symmetric auto- correlation, using Kullback discrimination of distributions,” Pattern Recognition Letters, vol. 16, no. 1, pp. 1-10, 1995.

M. Pietikäinen, T. Ojala and Z. Xu, “Rotation-invariant texture classification using feature distributions,” Pattern Recognition, vol. 33, pp. 43-52, 2000.

D. S. Larry, J. A. Steven and J. K. Aggarwal, “Texture analysis using generalized co-occurrence matrices,” Pattern Analysis and Machine Intelligence, IEEE Trans. on, vol. 3, pp. 251-259., 1979.

R. Porter and N. Canagarajah, “Robust rotation- invariant texture classification: wavelet, Gabor filter and GMRF based schemes,” in Vision, Image and Signal Processing, IEE Proceedings, 1997.

E. Provenzi, M. Fierro, A. Rizzi, L. De Carli and D. Marini, “Random Spray Retinex: A New Retinex Implementation to Investigate the Local Properties of the Model,” IEEE Transactions on Image Processing, vol. 16, no. 1, pp. 162-171, 2007.

A. Rizzi, C. Gatta and D. Marini, “From Retinex to Automatic Color Equalization: issues in developing a new algorithm for unsupervised color equalization,” J. Electronic Imaging, vol. 13, no. 1, pp. 75-84, 2004.

Lecca M.




How to Cite

Lecca, M. (2017) “Using Color and Local Binary Patterns for Texture Retrieval”, Cultura e Scienza del Colore - Color Culture and Science, 7, pp. 29–38. doi: 10.23738/ccsj.i72017.03.