Abstract
Feature extraction is an essential process for image data dimensionality reduction and classification. However, feature extraction is very difficult and often requires human intervention. Genetic Programming (GP) can achieve automatic feature extraction and image classification but the majority of existing methods extract low-level features from raw images without any image-related operations. Furthermore, the work on the combination of image-related operators/descriptors in GP for feature extraction and image classification is limited. This paper proposes a multi-layer GP approach (MLGP) to performing automatic high-level feature extraction and classification. A new program structure, a new function set including a number of image operators/descriptors and two region detectors, and a new terminal set are designed in this approach. The performance of the proposed method is examined on six different data sets of varying difficulty and compared with five GP based methods and 42 traditional image classification methods. Experimental results show that the proposed method achieves better or comparable performance than these baseline methods. Further analysis on the example programs evolved by the proposed MLGP method reveals the good interpretability of MLGP and gives insight into how this method can effectively extract high-level features for image classification.
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Pinz, A.: Object categorization. Found. Trends Comput. Graph. Vis. 1(4), 255–353 (2005)
Haralick, R.M., Shanmugam, K., et al.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 6, 610–621 (1973)
Ojala, T., Pietikainen, M., Harwood, D.: 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. IEEE (1994)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893. IEEE (2005)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Al-Sahaf, H., Al-Sahaf, A., Xue, B., Johnston, M., Zhang, M.: Automatically evolving rotation-invariant texture image descriptors by genetic programming. IEEE Trans. Evol. Comput. 21(1), 83–101 (2017)
Xue, B., Zhang, M.: Evolutionary feature manipulation in data mining/big data. ACM SIGEVOlution 10(1), 4–11 (2017)
Al-Sahaf, H., Zhang, M., Al-Sahaf, A., Johnston, M.: Keypoints detection and feature extraction: a dynamic genetic programming approach for evolving rotation-invariant texture image descriptors. IEEE Trans. Evol. Comput. (2017). https://doi.org/10.1109/TEVC.2017.2685639
Bi, Y., Zhang, M., Xue, B.: An automatic region detection and processing approach in genetic programming for binary image classification. In: The 32nd International Conference Image and Vision Computing New Zealand (IVCNZ 2017), pp. 1–6. IEEE (2017)
Fu, W., Johnston, M., Zhang, M.: Genetic programming for edge detection: a Gaussian-based approach. Soft Comput. 20(3), 1231–1248 (2016)
Al-Sahaf, H., Song, A., Neshatian, K., Zhang, M.: Extracting image features for classification by two-tier genetic programming. In: 2012 IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE (2012). https://doi.org/10.1109/CEC.2012.6256412
Al-Sahaf, H., Song, A., Neshatian, K., Zhang, M.: Two-tier genetic programming: towards raw pixel-based image classification. Expert Syst. Appl. 39(16), 12291–12301 (2012)
Atkins, D., Neshatian, K., Zhang, M.: A domain independent genetic programming approach to automatic feature extraction for image classification. In: 2011 IEEE Congress on Evolutionary Computation, pp. 238–245. IEEE (2011)
Lensen, A., Al-Sahaf, H., Zhang, M., Xue, B.: Genetic programming for region detection, feature extraction, feature construction and classification in image data. In: Heywood, M.I., McDermott, J., Castelli, M., Costa, E., Sim, K. (eds.) EuroGP 2016. LNCS, vol. 9594, pp. 51–67. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-30668-1_4
Zhang, M., Ciesielski, V.: Genetic programming for multiple class object detection. In: Foo, N. (ed.) AI 1999. LNCS (LNAI), vol. 1747, pp. 180–192. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-46695-9_16
Nandi, R., Nandi, A.K., Rangayyan, R.M., Scutt, D.: Classification of breast masses in mammograms using genetic programming and feature selection. Med. Biol. Eng. Compu. 44(8), 683–694 (2006)
Ain, Q.U., Xue, B., Al-Sahaf, H., Zhang, M.: Genetic programming for skin cancer detection in dermoscopic images. In: 2017 IEEE Congress on Evolutionary Computation, pp. 2420–2427. IEEE (2017)
Ryan, C., Fitzgerald, J., Krawiec, K., Medernach, D.: Image classification with genetic programming: building a stage 1 computer aided detector for breast cancer. In: Gandomi, A.H., Alavi, A.H., Ryan, C. (eds.) Handbook of Genetic Programming Applications, pp. 245–287. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20883-1_10
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Nene, S.A., Nayar, S.K., Murase, H., et al.: Columbia object image library (coil-20). Technical report, Columbia University (1996)
Agarwal, S., Awan, A., Roth, D.: Learning to detect objects in images via a sparse, part-based representation. IEEE Trans. Pattern. Anal. Mach. Intell. 26(11), 1475–1490 (2004)
Lyons, M., Akamatsu, S., Kamachi, M., Gyoba, J.: Coding facial expressions with Gabor wavelets. In: The Third IEEE International Conference on Automatic Face and Gesture Recognition, pp. 200–205. IEEE (1998)
Fei-Fei, L., Perona, P.: A Bayesian hierarchical model for learning natural scene categories. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 524–531. IEEE (2005)
Mallikarjuna, P., Targhi, A.T., Fritz, M., Hayman, E., Caputo, B., Eklundh, J.O.: The KTH-TIPS2 database. Computational Vision and Active Perception Laboratory (CVAP), Stockholm, Sweden (2006). http://www.nada.kth.se/cvap/databases/kth-tips
Welinder, P., Branson, S., Mita, T., Wah, C., Schroff, F., Belongie, S., Perona, P.: Caltech-UCSD Birds 200. Technical report CNS-TR-2010-001, California Institute of Technology (2010)
Haralick, R.M.: Statistical and structural approaches to texture. Proc. IEEE 67(5), 786–804 (1979)
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.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Khotanzad, A., Lu, J.H.: Classification of invariant image representations using a neural network. IEEE Trans. Acoust., Speech, Signal Process. 38(6), 1028–1038 (1990)
Fortin, F.A., De Rainville, F.M., Gardner, M.A., Parizeau, M., Gagné, C.: DEAP: evolutionary algorithms made easy. J. Mach. Learn. Res. 13, 2171–2175 (2012)
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Bi, Y., Xue, B., Zhang, M. (2018). An Automatic Feature Extraction Approach to Image Classification Using Genetic Programming. In: Sim, K., Kaufmann, P. (eds) Applications of Evolutionary Computation. EvoApplications 2018. Lecture Notes in Computer Science(), vol 10784. Springer, Cham. https://doi.org/10.1007/978-3-319-77538-8_29
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