Image recognition system based on novel measures of image similarity and cluster validity

We introduce an image recognition system that does not require availability of a complete training data. The system consists of a constrained K-Means clustering algorithm and an image recognition neural network. For finding similarity between images we use a novel image similarity measure and introduce a new image cluster validity measure to determine the most probable number of clusters. Extensive testing on several image datasets indicates good performance of the system.