Abstract
In this paper, a novel biologically inspired network for image recognition has been introduced. The Hierarchical model and X (HMAX) model and the extreme learning machine (ELM) are combined, to construct a five-layer feed-forward network: S1–C1–S2–C2–H. The previous four layers, originating from HMAX, provide robust feature representation of specific object, and the feature classification stage in the H layer is implemented with ELM. The HMAX model simulates the hierarchical processing mechanism in primate visual cortex, to calculate complex features representation. As a biological learning algorithm for generalized SLFNs, ELM learns much faster with good generalization performance, and performs well in classification applications. Four groups of experiments are performed on three datasets, and the results are compared with state-of-the-art techniques. Experimental results show that our proposed network has good performance with fast learning speed.
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