Interpretability based interest points detection
Proceedings of the 6th ACM international conference on Image and video retrieval
Interest point detection using imbalance oriented selection
Pattern Recognition
Multiobjective design of operators that detect points of interest in images
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Interest points based on maximization of distinctiveness
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Automated design of image operators that detect interest points
Evolutionary Computation
Evolving Local Descriptor Operators through Genetic Programming
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Scale Invariance for Evolved Interest Operators
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Selecting local region descriptors with a genetic algorithm for real-world place recognition
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Genetic programming as strategy for learning image descriptor operators
Intelligent Data Analysis
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The performance of high-level computer vision applications is tightly coupled with the low-level vision operations that are commonly required. Thus, it is advantageous to have low-level feature extractors that are optimal with respect to a desired performance criteria. This paper presents a novel approach that uses Genetic Programming as a learning framework that generates a specific type of low-level feature extractor: Interest Point Detector. The learning process is posed as an optimization problem. The optimization criterion is designed to promote the emergence of the detectors' geometric stability under different types of image transformations and global separability between detected points. This concept is represented by the operators repeatability rate [11]. Results prove that our approach is effective at automatically generating low-level feature extractors. This paper presents two different evolved operators: IPGP1 and IPGP2. Their performance is comparable with the Harris [5] operator given their excellent repeatability rate. Furthermore, the learning process was able to rediscover the DET corner detector proposed by Beaudet.