Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
PADO: a new learning architecture for object recognition
Symbolic visual learning
Proceedings of the European Conference on Genetic Programming
Automatic Construction of Tree-Structural Image Transformations Using Genetic Programming
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Efficient evolution of neural networks through complexification
Efficient evolution of neural networks through complexification
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Redundancy and computational efficiency in Cartesian genetic programming
IEEE Transactions on Evolutionary Computation
A study of evolutionary multiagent models based on symbiosis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Genetic Image Network for Image Classification
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Ensemble image classification method based on genetic image network
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
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A new method for automatic construction of image transformation, Feed Forward Genetic Image Network (FFGIN), is proposed in this paper. FFGIN evolves feed forward network structured image transformation automatically. Therefore, it is possible to straightforward execution of network structured image transformation. The genotype in FFGIN is a fixed length representation and consists of string which encode the image processing filter ID and connections of each node in the network. In order to verify the effectiveness of FFGIN, we apply FFGIN to the problem of automatic construction of image transformation which is "pasta segmentation" and compare with several method. From the experimental results, it is verified that FFGIN automatically constructs image transformation. Additionally, obtained structure by FFGIN is unique, and reuses the transformed images.