Object Recognition with an Optimized Ventral Stream Model Using Genetic Programming
Computational neuroscience is a discipline devoted to the study of brain function from an information processing standpoint. The ventral stream, also known as the “what” pathway, is widely accepted as the model for processing the visual information related to object identification. This paper proposes to evolve a mathematical description of the ventral stream where key features are identified in order to simplify the whole information processing. The idea is to create an artificial ventral stream by evolving the structure through an evolutionary computing approach. In previous research, the “what” pathway is described as being composed of two main stages: the interest region detection and feature description. For both stages a set of operations were identified with the aim of simplifying the total computational cost by avoiding a number of costly operations that are normally executed in the template matching and bag of feature approaches. Therefore, instead of applying a set of previously learned patches, product of an off-line training process, the idea is to enforce a functional approach. Experiments were carried out with a standard database and the results show that instead of 1200 operations, the new model needs about 200 operations.
KeywordsEvolutionary Artificial Ventral Stream Complex Designing System Heterogeneous and Hierarchical Genetic Programming
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