Learning Distributions of Image Features by Interactive Fuzzy Lattice Reasoning in Pattern Recognition Applications

This paper describes the recognition of image patterns based on novel representation learning techniques by considering higher-level (meta-)representations of numerical data in a mathematical lattice. In particular, the interest here focuses on lattices of (Type-1) Intervals' Numbers (INs), where an IN represents a distribution of image features including orthogonal moments. A neural classifier, namely fuzzy lattice reasoning (flr) fuzzy-ARTMAP (FAM), or flrFAM for short, is described for learning distributions of INs; hence, Type-2 INs emerge. Four benchmark image pattern recognition applications are demonstrated. The results obtained by the proposed techniques compare well with the results obtained by alternative methods from the literature. Furthermore, due to the isomorphism between the lattice of INs and the lattice of fuzzy numbers, the proposed techniques are straightforward applicable to Type-1 and/or Type-2 fuzzy systems. The far-reaching potential for deep learning in big data applications is also discussed.

[1]  Miin-Shen Yang,et al.  On similarity measures between intuitionistic fuzzy sets , 2008 .

[2]  Vassilis G. Kaburlasos,et al.  Piecewise-linear approximation of non-linear models based on probabilistically/possibilistically interpreted intervals' numbers (INs) , 2010, Inf. Sci..

[3]  George A. Papakostas,et al.  Thermal infrared face recognition based on lattice computing (LC) techniques , 2013, 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[4]  Valerie V. Cross,et al.  Unifying ontological similarity measures: A theoretical and empirical investigation , 2013, Int. J. Approx. Reason..

[5]  Athanasios Kehagias,et al.  Fuzzy Inference System (FIS) Extensions Based on the Lattice Theory , 2014, IEEE Transactions on Fuzzy Systems.

[6]  Takeo Kanade,et al.  Facial Expression Recognition , 2011, Handbook of Face Recognition.

[7]  김용수,et al.  Extreme Learning Machine 기반 퍼지 패턴 분류기 설계 , 2015 .

[8]  Vassilis G. Kaburlasos,et al.  Binary Image 2D Shape Learning and Recognition Based on Lattice-Computing (LC) Techniques , 2011, Journal of Mathematical Imaging and Vision.

[9]  Hongming Zhou,et al.  Extreme Learning Machines [Trends & Controversies] , 2013 .

[10]  Manuel Graña,et al.  A lattice computing approach to Alzheimer's disease computer assisted diagnosis based on MRI data , 2015, Neurocomputing.

[11]  George A. Papakostas,et al.  Two Fuzzy Lattice Reasoning (FLR) Classifiers and their Application for Human Facial Expression Recognition , 2014, J. Multiple Valued Log. Soft Comput..

[12]  Barbara Hammer,et al.  Efficient approximations of robust soft learning vector quantization for non-vectorial data , 2015, Neurocomputing.

[13]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[14]  Jochen Triesch,et al.  Robust classification of hand postures against complex backgrounds , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[15]  Humberto Bustince,et al.  Theta-Fuzzy Associative Memories (Theta-FAMs) , 2015, IEEE Transactions on Fuzzy Systems.

[16]  Victor C. M. Leung,et al.  Extreme Learning Machines [Trends & Controversies] , 2013, IEEE Intelligent Systems.

[17]  Manuel Graña,et al.  Random forest active learning for AAA thrombus segmentation in computed tomography angiography images , 2014, Neurocomputing.

[18]  George A. Papakostas,et al.  Intervals' Numbers (INs) interpolation/extrapolation , 2013, 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[19]  Chee Kheong Siew,et al.  Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.

[20]  Peter Sussner,et al.  Quantale-based autoassociative memories with an application to the storage of color images , 2013, Pattern Recognit. Lett..

[21]  George A. Papakostas,et al.  Lattice computing (LC) meta-representation for pattern classification , 2014, 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[22]  Peter Sussner,et al.  Tunable equivalence fuzzy associative memories , 2016, Fuzzy Sets Syst..

[23]  Yaling Dou,et al.  Solving the fuzzy shortest path problem using multi-criteria decision method based on vague similarity measure , 2012, Appl. Soft Comput..

[24]  George A. Papakostas,et al.  Distance and similarity measures between intuitionistic fuzzy sets: A comparative analysis from a pattern recognition point of view , 2013, Pattern Recognit. Lett..

[25]  George Papakostas,et al.  Moments and Moment Invariants - Theory and Applications , 2014 .

[26]  George A. Papakostas,et al.  Lattice Computing Extension of the FAM Neural Classifier for Human Facial Expression Recognition , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[27]  Vassilis G. Kaburlasos,et al.  A Lattice-Computing ensemble for reasoning based on formal fusion of disparate data types, and an industrial dispensing application , 2014, Inf. Fusion.

[28]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Michael J. Lyons,et al.  Coding facial expressions with Gabor wavelets , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.