Image Parsing with a Three-State Series Neural Network Classifier

We propose a three-state series neural network for effective propagation of context and uncertainty information for image parsing. The activation functions used in the proposed model have three states instead of the normal two states. This makes the neural network more flexible than the two-state neural network, and allows for uncertainty to be propagated through the stages. In other words, decisions about difficult pixels can be left for later stages which have access to more contextual information than earlier stages. We applied the proposed method to three different datasets and experimental results demonstrate higher performance of the three-state series neural network.