Abstract
GP has a flexible representation and can address binary image classification in many possible ways. This chapter proposes a GP-based approach with a multi-layer representation to achieve simultaneous and automatic region detection, feature extraction, feature construction, and image classification. Each layer can have a different number of functions for the corresponding task. The effectiveness of the proposed approach is verified on six different image classification tasks of varying difficulty in comparisons with a large number of baseline methods. Further analysis shows potential interpretability of the solutions/classifiers evolved by the proposed approach.
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References
Agarwal S, Awan A, Roth D (2004) Learning to detect objects in images via a sparse, part-based representation. IEEE Trans Pattern Anal Mach Intell 26(11):1475–1490
Al-Sahaf H (2017) Genetic programming for automatically synthesising robust image descriptors with a small number of instances. PhD thesis, Victoria University of Wellington, New Zealand
Al-Sahaf H, Song A, Neshatian K, Zhang M (2012) Extracting image features for classification by two-tier genetic programming. In: Proceedings of IEEE congress on evolutionary computation, pp 12291–12301
Al-Sahaf H, Song A, Neshatian K, Zhang M (2012b) Two-tier genetic programming: towards raw pixel-based image classification. Expert Syst Appl 39(16):12291–12301
Atkins D, Neshatian K, Zhang M (2011) A domain independent genetic programming approach to automatic feature extraction for image classification. In: Proceedings of IEEE congress on evolutionary computation, pp 238–245
Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition, vol 1, pp 886–893
Fei-Fei L, Perona P (2005) A bayesian hierarchical model for learning natural scene categories. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition, vol 2, pp 524–531
Fortin FA, De Rainville FM, Gardner MA, Parizeau M, Gagné C (2012) DEAP: evolutionary algorithms made easy. J Mach Learn Res 13(Jul):2171–2175
Haralick RM (1979) Statistical and structural approaches to texture. Proc IEEE 67(5):786–804
Khotanzad A, Lu JH (1990) Classification of invariant image representations using a neural network. IEEE Trans Acoust, Speech, Signal Process 38(6):1028–1038
Lensen A, Al-Sahaf H, Zhang M, Xue B (2016) Genetic programming for region detection, feature extraction, feature construction and classification in image data. In: Proceedings of European conference on genetic programming, Springer, pp 51–67
Lyons M, Akamatsu S, Kamachi M, Gyoba J (1998) Coding facial expressions with gabor wavelets. In: Proceedings of the 3rd IEEE international conference on automatic face and gesture recognition, pp 200–205
Mallikarjuna P, Targhi AT, Fritz M, Hayman E, Caputo B, Eklundh JO (2006) The kth-tips2 database. Computational Vision and Active Perception Laboratory (CVAP), Stockholm, Sweden. http://www.nada.kth.se/cvap/databases/kth-tips
Montana DJ (1995) Strongly typed genetic programming. Evol Comput 3(2):199–230
Nandi R, Nandi AK, Rangayyan RM, Scutt D (2006) Classification of breast masses in mammograms using genetic programming and feature selection. Med Biol Eng Comput 44(8):683–694
Nene SA, Nayar SK, Murase H, et al. (1996) Columbia object image library (coil-20). Technical report, Columbia Universty
Ojala T, Pietikainen M, Harwood D (1994) Performance evaluation of texture measures with classification based on kullback discrimination of distributions. In: Proceedings of the 12th IAPR international conference on pattern recognition, vol 1, pp 582–585
Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830
Ryan C, Fitzgerald J, Krawiec K, Medernach D (2015) Image classification with genetic programming: Building a stage 1 computer aided detector for breast cancer. Handbook of genetic programming applications. Springer, Berlin, pp 245–287
Welinder P, Branson S, Mita T, Wah C, Schroff F, Belongie S, Perona P (2010) Caltech-ucsd birds 200. Technical report CNS-TR-2010-001, California Institute of Technology
Zhang M, Ciesielski V (1999) Genetic programming for multiple class object detection. In: Proceedings of Australian joint conference on artificial intelligence, Springer, vol 1747, pp 180–192
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Bi, Y., Xue, B., Zhang, M. (2021). Multi-layer Representation for Binary Image Classification. In: Genetic Programming for Image Classification. Adaptation, Learning, and Optimization, vol 24. Springer, Cham. https://doi.org/10.1007/978-3-030-65927-1_4
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DOI: https://doi.org/10.1007/978-3-030-65927-1_4
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