Automatically Evolving Rotation-invariant Texture Image Descriptors by Genetic Programming

Al-Sahaf, H., Al-Sahaf, A., Xue, B., Johnston, Mark and Zhang, M. (2017) Automatically Evolving Rotation-invariant Texture Image Descriptors by Genetic Programming. IEEE Transactions on Evolutionary Computation, 21 (1). pp. 83-101. ISSN Print 1089-778X Online 1941-0026

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Abstract

In computer vision, training a model that performs classification effectively is highly dependent on the extracted features, and the number of training instances. Conventionally, feature detection and extraction are performed by a domain-expert who, in many cases, is expensive to employ and hard to find. Therefore, image descriptors have emerged to automate these tasks. However, designing an image descriptor still requires domain-expert intervention. Moreover, the majority of machine learning algorithms require a large number of training examples to perform well. However, labelled data is not always available or easy to acquire, and dealing with a large dataset can dramatically slow down the training process. In this paper, we propose a novel Genetic Programming based method that automatically synthesises a descriptor using only two training instances per class. The proposed method combines arithmetic operators to evolve a model that takes an image and generates a feature vector. The performance of the proposed method is assessed using six datasets for texture classification with different degrees of rotation, and is compared with seven domain-expert designed descriptors. The results show that the proposed method is robust to rotation, and has significantly outperformed, or achieved a comparable performance to, the baseline methods.

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