Abstract:

According to the related research of the brain of neural structures and cognitive function which apperceive the external environment, the brain-like model of multimedia neural cognitive computing (MNCC) is built. The model simulates the basic neural structures and the cognition of brain, such as the sensory information perception, the cognition of the neocortex column, the attention control structure of the thalamus, the hippocampus memory and emotional control circuits of the limbic system, and a scene classification MNCC-based algorithm for high-resolution remote sensing images is established. Firstly, the algorithm extracts sparse activation features of the deep neural network from the sub-blocks image after affine transformation, and gets initial category of the sub-blocks image with the probability topic model, then controls features extraction of the saliency area by sub-block classification error.Secondly, the temporal-spatial features of scene semantic are acquired by sub-blocks context, then sub-blocks categorization and scene pre-classification are processed to obtain initial scene labels. Finally, the scene pre-classification error is used for construction rewards function to control and select the most discrimination sparse activation features of deep neural network, and the final scene label  is obtained by the incremental reinforced ensemble learning algorithm. Experiment results show that the MNCC algorithm presented in this paper has better performance of scene classification on the two standard highresolution remote sensing scene datasets.