Automatically Evolving Rotation-Invariant Texture Image Descriptors by Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{Al-Sahaf:2017a:ieeeTEC,

• author = "Harith Al-Sahaf and Ausama Al-Sahaf and Bing Xue and Mark Johnston and Mengjie Zhang",
• title = "Automatically Evolving Rotation-Invariant Texture Image Descriptors by Genetic Programming",
• journal = "IEEE Transactions on Evolutionary Computation",
• year = "2017",
• volume = "21",
• number = "1",
• pages = "83--101",
• month = feb,
• keywords = "genetic algorithms, genetic programming, Classification, feature extraction, image descriptor, keypoint detection",
• ISSN = "1089-778X",
• DOI = "doi:10.1109/TEVC.2016.2577548",
• size = "19 pages",
• 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.",
• notes = "also known as \cite{7486119}",

}

Genetic Programming entries for Harith Al-Sahaf Ausama Al-Sahaf Bing Xue Mark Johnston Mengjie Zhang

Citations