A Computational Visual Neuroscience Model for Object Recognition

Abstract

In this study with the inspirations from both neuroscience and computer science, a combinatorial framework for object recognition was proposed having benefited from the advantages of both biologically-inspired HMAX_S architecture model for feature extraction and Extreme Learning Machine (ELM) as a classifier. HMAX model is a feed-forward hierarchical structure resembling the ventral pathway in the visual cortex of the brain and ELM is a powerful neural network, which randomly chooses hidden nodes and specifies analytically the single-hidden layer. ELM theories conjecture that this randomness may be true for biological learning in animal brains. It should be noted that the principle reason of using ELM is mainly as a result of its biological structure in order to imitate the biological object recognition system of mammalians and partly for its incredible speed which drastically lessens the runtime. Classification results are reported in Caltech101 dataset, at the focal point with its combinatorial framework serving considerable improvements over latest studies in both classification rate (96.39%) and the low runtime (0.417s).