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Integration between biology and information science benefits both fields.Many related models have been proposed, such as computational visual cognitionmodels, computational motor control models, integrations of both and so on. Ingeneral, the robustness and precision of recognition is one of the key problemsfor object recognition models.  In this paper, inspired by features of human recognition process and theirbiological mechanisms, a new integrated and dynamic framework is proposed tomimic the semantic extraction, concept formation and feature re-selection inhuman visual processing. The main contributions of the proposed model are asfollows:  (1) Semantic feature extraction: Local semantic features are learnt fromepisodic features that are extracted from raw images through a deep neuralnetwork;  (2) Integrated concept formation: Concepts are formed with local semanticinformation and structural information learnt through network.  (3) Feature re-selection: When ambiguity is detected during recognitionprocess, distinctive features according to the difference between ambiguouscandidates are re-selected for recognition.  Experimental results on hand-written digits and facial shape dataset showthat, compared with other methods, the new proposed model exhibits higherrobustness and precision for visual recognition, especially in the conditionwhen input samples are smantic ambiguous. Meanwhile, the introduced biologicalmechanisms further strengthen the interaction between neuroscience andinformation science.

A Novel Biologically Mechanism-Based Visual Cognition Model--Automatic  Extraction of Semantics, Formation of Integrated Concepts and Re-selection  Features for Ambiguity