The following two phases are involved during any classification process: training and decision. Really, the combination of the outputs provided by the two individual classifiers is carried out during the decision phase, as we will explain later. Given a set of training data, scattered through the tri-dimensional RGB data space and assuming known the number of clusters and the distribution of the samples into the clusters, both BP and FC individual classifiers estimate their associated parameters. Based on these estimated parameters, during the decision phase, each individual classifier provides for each pixel to be classified, a support of belonging to a cluster, BP provides probabilities and FC membership degrees, i.
e., continuous outputs.
Because the number of classes is known, we build a network of nodes netj for each class wj, where each node i in the netj is identified as a pixel location i �� (x, y) in the image which is to be classified. Each node i is initialized in the netj with the output probability, provided by BP, that the node belongs to the class wj. This is the initial state value for the node i in the netj. Each state is later iteratively updated through the Deterministic Simulated Annealing (DSA) optimization strategy taking into account the previous states and two types of external influences exerted by other nodes on its neighbourhood. The external influences are mapped as consistencies under two terms: regularization and contextual.
These terms are clique potentials of an underlying Markov Random Field model  and they both involve a kind of human perception.
Indeed, the tri-dimensional scenes are captured by the imaging sensor and mapped in the bi-dimensional space, although the third dimension is lost under this mapping, the spatial grouping of the regions is preserved, and they are Brefeldin_A Anacetrapib visually perceived grouped together like in the real scene.The above allows the application of the Gestalt principles of psychology [24,25], specifically: similarity, proximity and connectedness. The similarity principle states that similar pixels tend to be grouped together. The proximity principle states that pixels near to one another tend to be grouped together.
The connectedness states that the pixels belonging to the same region are spatially connected. The proximity and connectedness principles justify the choice of the neighbourhood for defining the regularization and contextual terms and the similarity establishes the analogies in the supports received by the pixels in the neighbourhood coming from the individual classifiers. From the point of view of the combination of classifiers the most relevant term is the regularization one.