Resilient Image Feature Description through Evolution



Feature description is an important stage in many different vision algorithms. Image features detected by various detectors can be described using descriptors either with a binary or floating-point structure. This study presents the use of evolutionary algorithms, namely Genetic Algorithms (GA), in order to improve the robustness of the feature descriptors against increasing levels of photographic distortions such as noise or JPEG compression. Original feature descriptors were evolved in order to reduce the descriptor distance for the mentioned test cases. Results, tested using a statistical framework, suggest that the evolved descriptors offer better matching performance for two state-of-the-art descriptors.


Feature Description; Matching; Genetic Algorithms; Binary Descriptors; Floating Point Descriptors; Optimization

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Submitted: 2017-03-11 17:07:19
Published: 2017-06-29 13:43:01
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© Prof.Dr. Ismail SARITAS 2013-2019     -    Address: Selcuk University, Faculty of Technology 42031 Selcuklu, Konya/TURKEY.