LBP parameter tuning for texture analysis of lace images

Analysis of lace texture images is a challenging problem because the lace is a soft and extensible material and can be easily deformed. This paper investigates a whole system for lace classification. A first step, based on Otsu's segmentation method, allows to remove the background. Then the lace texture is characterized using local binary patterns (LBP). In order to be robust against rotation the Fourier Transform is applied on LBP histograms. The magnitude spectrum of this transform is then used as a feature vector. LBP descriptor parameters, including radius and number of neighbors, are adjusted in order to improve their relevance. The experiments show that the features based on LBP, with appropriate settings, produced good results in supervised and unsupervised contexts.

[1]  Alice Porebski,et al.  Texture analysis of lace images using histogram and local binary patterns under rotation variation , 2014, International Image Processing, Applications and Systems Conference.

[2]  Elke Achtert,et al.  Evaluation of Clusterings -- Metrics and Visual Support , 2012, 2012 IEEE 28th International Conference on Data Engineering.

[3]  Matti Pietikäinen,et al.  Computer Vision Using Local Binary Patterns , 2011, Computational Imaging and Vision.

[4]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[6]  Matti Pietikäinen,et al.  Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features , 2009, SCIA.

[7]  Michael I. Jordan,et al.  On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.

[8]  Jiawei Han,et al.  Document clustering using locality preserving indexing , 2005, IEEE Transactions on Knowledge and Data Engineering.

[9]  M. Meilă.  Comparing clusterings---an information based distance , 2007 .

[10]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[12]  Matti Pietikäinen,et al.  Rotation-Invariant Image and Video Description With Local Binary Pattern Features , 2012, IEEE Transactions on Image Processing.

[13]  Matti Pietikäinen,et al.  A Generalized Local Binary Pattern Operator for Multiresolution Gray Scale and Rotation Invariant Texture Classification , 2001, ICAPR.