  Integration between biology and information science benefits both fields.
Many related models have been proposed, such as computational visual cognition
models, computational motor control models, integrations of both and so on. In
general, the robustness and precision of recognition is one of the key problems
for object recognition models.
  In this paper, inspired by features of human recognition process and their
biological mechanisms, a new integrated and dynamic framework is proposed to
mimic the semantic extraction, concept formation and feature re-selection in
human visual processing. The main contributions of the proposed model are as
follows:
  (1) Semantic feature extraction: Local semantic features are learnt from
episodic features that are extracted from raw images through a deep neural
network;
  (2) Integrated concept formation: Concepts are formed with local semantic
information and structural information learnt through network.
  (3) Feature re-selection: When ambiguity is detected during recognition
process, distinctive features according to the difference between ambiguous
candidates are re-selected for recognition.
  Experimental results on hand-written digits and facial shape dataset show
that, compared with other methods, the new proposed model exhibits higher
robustness and precision for visual recognition, especially in the condition
when input samples are smantic ambiguous. Meanwhile, the introduced biological
mechanisms further strengthen the interaction between neuroscience and
information science.
