Image descriptor: A genetic programming approach to multiclass texture classification

Texture classification is an essential task in computer vision that aims at grouping instances that have a similar repetitive pattern into one group. Detecting texture primitives can be used to discriminate between materials of different types. The process of detecting prominent features from the texture instances represents a cornerstone step in texture classification. Moreover, building a good model using a few training instances is difficult. In this study, a genetic programming (GP) descriptor is proposed for the task of multiclass texture classification. The proposed method synthesises a set of mathematical formulas relying on the raw pixel values and a sliding window of a predetermined size. Furthermore, only two instances per class are used to automatically evolve a descriptor that has the potential to effectively discriminate between instances of different textures using a simple instance-based classifier to perform the classification task. The performance of the proposed approach is examined using two widely-used data sets, and compared with two GP-based and nine well-known non-GP methods. Furthermore, three hand-crafted domain-expert designed feature extraction methods have been used with the non-GP methods to examine the effectiveness of the proposed method. The results show that the proposed method has significantly outperformed all these other methods on both data sets, and the new method evolves a descriptor that is capable of achieving significantly better performance compared to hand-crafted features.

[1]  Mengjie Zhang,et al.  Genetic programming for improving image descriptors generated using the scale-invariant feature transform , 2012, IVCNZ '12.

[2]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

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

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

[5]  Vincent Lepetit,et al.  DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Gustaf Kylberg,et al.  Kylberg Texture Dataset v. 1.0 , 2011 .

[7]  J. L. Hodges,et al.  Discriminatory Analysis - Nonparametric Discrimination: Consistency Properties , 1989 .

[8]  F. Wilcoxon Individual Comparisons by Ranking Methods , 1945 .

[9]  Grant Dick,et al.  Implicitly Controlling Bloat in Genetic Programming , 2010, IEEE Transactions on Evolutionary Computation.

[10]  Walter Alden Tackett,et al.  Genetic Programming for Feature Discovery and Image Discrimination , 1993, ICGA.

[11]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[12]  Jana Reinhard,et al.  Textures A Photographic Album For Artists And Designers , 2016 .

[13]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[14]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[15]  Leonardo Trujillo,et al.  Automated Design of Image Operators that Detect Interest Points , 2008, Evolutionary Computation.

[16]  Yaochu Jin,et al.  Evolutionary Multiobjective Image Feature Extraction in the Presence of Noise , 2015, IEEE Transactions on Cybernetics.

[17]  Anil K. Jain,et al.  Texture Analysis , 2018, Handbook of Image Processing and Computer Vision.

[18]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[19]  Georgios Lappas,et al.  Estimating the Size of Neural Networks from the Number of Available Training Data , 2007, ICANN.

[20]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[21]  Vic Ciesielski,et al.  Representing classification problems in genetic programming , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[22]  Konstantinos N. Plataniotis,et al.  Distance measures for color image retrieval , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[23]  Ian Witten,et al.  Data Mining , 2000 .

[24]  Paul W. Fieguth,et al.  Extended local binary patterns for texture classification , 2012, Image Vis. Comput..

[25]  Chih-Jen Lin,et al.  Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.

[26]  David J. Montana,et al.  Strongly Typed Genetic Programming , 1995, Evolutionary Computation.

[27]  Leonardo Trujillo,et al.  Evolutionary-computer-assisted design of image operators that detect interest points using genetic programming , 2011, Image Vis. Comput..

[28]  Geoff Holmes,et al.  Multiclass Alternating Decision Trees , 2002, ECML.

[29]  Victor Ciesielski,et al.  Genetic Programming for Multiple Class Object Detection , 1999, Australian Joint Conference on Artificial Intelligence.

[30]  Matti Pietikäinen,et al.  Performance evaluation of texture measures with classification based on Kullback discrimination of distributions , 1994, Proceedings of 12th International Conference on Pattern Recognition.

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

[32]  Mark Johnston,et al.  Genetic Programming for Multiclass Texture Classification Using a Small Number of Instances , 2014, SEAL.

[33]  Gustavo Olague,et al.  Evolutionary learning of local descriptor operators for object recognition , 2009, GECCO.

[34]  Alexander Kadyrov,et al.  The trace transform as a tool to invariant feature construction , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[35]  Victor Ciesielski,et al.  Towards Genetic Programming for Texture Classification , 2001, Australian Joint Conference on Artificial Intelligence.

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

[37]  Leonardo Trujillo,et al.  A Genetic Programming Approach to the Design of Interest Point Operators , 2009, Bio-inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition.

[38]  Victor Ciesielski,et al.  A Domain-Independent Window Approach to Multiclass Object Detection Using Genetic Programming , 2003, EURASIP J. Adv. Signal Process..

[39]  Mark Johnston,et al.  Genetic Programming Evolved Filters from a Small Number of Instances for Multiclass Texture Classification , 2014, IVCNZ '14.

[40]  Ling Shao,et al.  Feature Learning for Image Classification Via Multiobjective Genetic Programming , 2014, IEEE Transactions on Neural Networks and Learning Systems.