Hybrid Statistical Feature Extraction Method LPB – EDMS for Shape Recognition Dataset

Invariant descriptor for shape and texture image recognition usage is an essential branch of pattern recognition. It is made up of techniques that aim at extracting information from shape images via human knowledge and works. The descriptors need to have strong Local Binary Pattern (LBP) in order to encode the information distinguishing them. Local Binary Pattern (LBP) ensures encoding global and local information and scaling invariance by introducing a look-up table to reflect the uniformity structure of an object. It is needed as the edge direction matrices (EDMS) only apply global invariant descriptor which employs first and secondary order relationships. The main objective of this paper is the need of improved recognition capabilities which achieved by the combining LBP and EDMS. Working together, these two descriptors will add advantages to the program and enable the researcher to investigate the weaknesses of each one. Two classifiers are used: multi-layer neural network and random forest. The techniques used in this paper are compared with Gray-Level Co-occurrence matrices (GLCM-EDMS) and Scale Invariant Feature Transform (SIFT) by using two benchmark dataset: MPEG-7 CE-Shape-1 for shape and Arabic calligraphy for texture. The experiments have shown the superiority of the introduced descriptor over the GLCMEDMS and the SIFT. KeywordsFeature Extraction, Local Binary Patterns(LBP), Edge Direction Matrixes(EDMS), Classification.

[1]  Texture Analysis on Weather Radar Images of Severe Convective Precipitation in the Arid Area of Ningxia , 2008, 2008 Congress on Image and Signal Processing.

[2]  Siti Norul Huda Sheikh Abdullah,et al.  Character recognition based on global feature extraction , 2011, Proceedings of the 2011 International Conference on Electrical Engineering and Informatics.

[3]  Giacomo Patrizi,et al.  Formal methods in pattern recognition: A review , 2000, Eur. J. Oper. Res..

[4]  Huchuan Lu,et al.  Multilinear analysis based on image texture for face recognition , 2008, 2008 19th International Conference on Pattern Recognition.

[5]  Khairuddin Omar,et al.  A Statistical Global Feature Extraction Method for Optical Font Recognition , 2011, ACIIDS.

[6]  V. A. Gaikwad.  An Overview of Character Recognition Focused On Off- line Handwriting , 2008 .

[7]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[8]  Anil K. Jain,et al.  Introduction to Pattern Recognition , 2007 .

[9]  I. Biederman.  Recognition-by-components: a theory of human image understanding. , 1987, Psychological review.

[10]  Peng Wu,et al.  The Research of Satellite Cloud Image Recognition Base on Variational Method and Texture Feature Analysis , 2007, 2007 2nd IEEE Conference on Industrial Electronics and Applications.

[11]  Rowena Cristina L. Guevara,et al.  Combining Local and Global Features for Offline Handwriting Recognition , 2005 .

[12]  C. SantoshK..  Character Recognition based on DTW – Radon , 2011 .

[13]  Jie Liu,et al.  Pattern Recognition: An overview , 2006 .

[14]  Carolyn Korsmeyer.  Rosalind W. Picard, Affective Computing , 2004, Minds and Machines.

[15]  Xiangde Zhang,et al.  Noise detection of iris image based on texture analysis , 2009, 2009 Chinese Control and Decision Conference.

[16]  Marzuki Khalid,et al.  License plate recognition based on geometry features topological analysis and support vector machine , 2007 .

[17]  Siti Norul Huda Sheikh Abdullah,et al.  Geometrical-matrix feature extraction for on-line handwritten characters recognition , 2013 .

[18]  Ajai Jain,et al.  The Handbook of Pattern Recognition and Computer Vision , 1993 .

[19]  S. Shivashankar,et al.  Writer identification in a handwritten document image using texture features , 2010, 2010 International Conference on Signal and Image Processing.

[20]  James L. McClelland.  Is a Machine Realization of Truly Human-Like Intelligence Achievable? , 2009, Cognitive Computation.

[21]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[22]  Xudong Jiang,et al.  Feature extraction for image recognition and computer vision , 2009, 2009 2nd IEEE International Conference on Computer Science and Information Technology.

[23]  Nafiz Arica,et al.  An overview of character recognition focused on off-line handwriting , 2001, IEEE Trans. Syst. Man Cybern. Syst..

[24]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[25]  Miguel A. Ferrer,et al.  Low Cost Multimodal Biometric identification System Based on Hand Geometry, Palm and Finger Print Texture , 2007, 2007 41st Annual IEEE International Carnahan Conference on Security Technology.

[26]  Yong Haur Tay,et al.  An offline cursive handwritten word recognition system , 2001, Proceedings of IEEE Region 10 International Conference on Electrical and Electronic Technology. TENCON 2001 (Cat. No.01CH37239).

[27]  Dimitris E. Koulouriotis,et al.  Moment-based local binary patterns: A novel descriptor for invariant pattern recognition applications , 2013, Neurocomputing.

[28]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[29]  Sridha Sridharan,et al.  Texture for script identification , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Maria Petrou,et al.  Image processing - dealing with texture , 2020 .

[31]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  Zhuowen Tu,et al.  Learning Context-Sensitive Shape Similarity by Graph Transduction , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Slobodan Ribarić,et al.  Introduction to Pattern Recognition , 1988 .