Keypoints Detection and Feature Extraction: A Dynamic Genetic Programming Approach for Evolving Rotation-invariant Texture Image Descriptors

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@Article{Al-Sahaf:2017:ieeeTEC,

• author = "Harith Al-Sahaf and Mengjie Zhang and Ausama Al-Sahaf and Mark Johnston",
• journal = "IEEE Transactions on Evolutionary Computation",
• title = "Keypoints Detection and Feature Extraction: A Dynamic Genetic Programming Approach for Evolving Rotation-invariant Texture Image Descriptors",
• year = "2017",
• volume = "21",
• number = "6",
• pages = "825--844",
• month = dec,
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1089-778X",
• URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7885048",
• DOI = "doi:10.1109/TEVC.2017.2685639",
• abstract = "The goodness of the features extracted from the instances and the number of training instances are two key components in machine learning, and building an effective model is largely affected by these two factors. Acquiring a large number of training instances is very expensive in some situations such as in the medical domain. Designing a good feature set, on the other hand, is very hard and often requires domain expertise. In computer vision, image descriptors have emerged to automate feature detection and extraction; however, domain-expert intervention is typically needed to develop these descriptors. The aim of this paper is to use Genetic Programming to automatically construct a rotation-invariant image descriptor by synthesising a set of formulae using simple arithmetic operators and first-order statistics, and determining the length of the feature vector simultaneously using only two instances per class. Using seven texture classification image datasets, the performance of the proposed method is evaluated and compared against eight domain-expert hand-crafted image descriptors. Quantitatively, the proposed method has significantly outperformed, or achieved comparable performance to, the competitor methods. Qualitatively, the analysis shows that the descriptors evolved by the proposed method can be interpreted.",
• notes = "Also known as \cite{7885048}",

}

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

Citations