Scene image classfying via the Partially Connected Neural Network

This paper presented a new method for scene images classification via Partially Connected Neural Network. The neural network has a mesh structure in which each neuron maintain a fixed number of connections with other neurons. In training, the evolutionary computation method was used to optimize the connection target neurons and its connection weights. The model is able to receive a large number of input neurons and make it possible that classification of scene images needed neither any image preprocessing nor any feature extraction. Thus, the new method overcome the bug that loss and uncertainty of image information brought by man-made feature selection in the past. A large-scale GPU parallel computing method was used to accelerate neural network training. Though experiments of the method, we report a satisfactory classification performance especially for the scene images which contain artificial objects.