Feature extraction, feature selection and machine learning for image classification: A case study

This paper presents feature extraction, feature selection and machine learning-based classification techniques for pollen recognition from images. The number of images is small compared both to the number of derived quantitative features and to the number of classes. The main subject is investigation of the effectiveness of 11 feature extraction/feature selection algorithms and of 12 machine learning-based classifiers. It is found that some of the specified feature extraction/selection algorithms and some of the classifiers exhibited consistent behavior for this dataset.

[1]  Derek C. Rose,et al.  Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier] , 2010, IEEE Computational Intelligence Magazine.

[2]  Eibe Frank,et al.  Logistic Model Trees , 2003, ECML.

[3]  Luca Maria Gambardella,et al.  Deep, Big, Simple Neural Nets for Handwritten Digit Recognition , 2010, Neural Computation.

[4]  Masao Fukushima,et al.  Tabu search for attribute reduction in rough set theory , 2008, Soft Comput..

[5]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[6]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[7]  Masoud Nikravesh,et al.  Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing) , 2006 .

[8]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[9]  Jianqing Fan,et al.  A Selective Overview of Variable Selection in High Dimensional Feature Space. , 2009, Statistica Sinica.

[10]  Rich Caruana,et al.  An empirical comparison of supervised learning algorithms , 2006, ICML.

[11]  Jose Miguel Puerta,et al.  Fast wrapper feature subset selection in high-dimensional datasets by means of filter re-ranking , 2012, Knowl. Based Syst..

[12]  Alex Graves,et al.  Supervised Sequence Labelling with Recurrent Neural Networks , 2012, Studies in Computational Intelligence.

[13]  Monique Thonnat,et al.  Object recognition from 3D blurred images , 2002, Object recognition supported by user interaction for service robots.

[14]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[15]  G. E. Taylor,et al.  Computerized identification of pollen grains by texture analysis , 1990 .

[16]  Haijia Shi.  Best-first Decision Tree Learning , 2007 .

[17]  João Gama,et al.  Functional Trees , 2001, Machine Learning.

[18]  Tom M. Mitchell,et al.  The Need for Biases in Learning Generalizations , 2007 .

[19]  Arno Formella,et al.  Pollen classification using brightness-based and shape-based descriptors , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[20]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..

[21]  Yoshua Bengio,et al.  Inference for the Generalization Error , 1999, Machine Learning.

[22]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

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

[24]  Ruili Wang,et al.  Airborne pollen texture discrimination using wavelet transforms in combination with cooccurrence matrices , 2005, Int. J. Intell. Syst. Technol. Appl..

[25]  Geoff Hulten,et al.  Mining time-changing data streams , 2001, KDD '01.

[26]  Santiago T. Pérez,et al.  Image Processing for Pollen Classification , 2012 .

[27]  Y-Lan Boureau,et al.  Learning Convolutional Feature Hierarchies for Visual Recognition , 2010, NIPS.

[28]  Riccardo Poli,et al.  Geometric Particle Swarm Optimisation , 2007, EuroGP.

[29]  Eibe Frank,et al.  Large-scale attribute selection using wrappers , 2009, 2009 IEEE Symposium on Computational Intelligence and Data Mining.

[30]  Luca Maria Gambardella,et al.  Convolutional Neural Network Committees for Handwritten Character Classification , 2011, 2011 International Conference on Document Analysis and Recognition.

[31]  R. Dell’Anna,et al.  Pollen discrimination and classification by Fourier transform infrared (FT-IR) microspectroscopy and machine learning , 2009, Analytical and bioanalytical chemistry.

[32]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[33]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

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

[35]  Walter John Treloar.  Digital image processing techniques and their application to the automation of palynology , 1992 .

[36]  Ian H. Witten,et al.  Generating Accurate Rule Sets Without Global Optimization , 1998, ICML.