Statistical cues for domain specific image segmentation with performance analysis

This paper investigates the use of colour and texture cues for segmentation of images within two specified domains. The first is the Sowerby dataset, which contains one hundred colour photographs of country roads in England that have been interactively segmented and classified into six classes-edge, vegetation, air, road, building, and other. The second domain is a set of thirty five-images, taken in San Francisco, which have been interactively segmented into similar classes. In each domain we learn the joint probability distributions of filter responses, based on colour and texture, for each class. These distributions are then used for classification. We restrict ourselves to a limited number of filters in order to ensure that the learnt filter responses do not overfit the training data (our region classes are chosen so as to ensure that there is enough data to avoid over fitting). We do performance analysis on the two datasets by evaluating the false positive and false negative error rates for the classification. This shows that the learnt models achieve high accuracy in classifying individual pixels into those classes for which the filter responses are approximately spatially homogeneous (i.e. road, vegetation, and air but not edge and building). A more sensitive performance measure, the Chernoff information, is calculated in order to quantify how well the cues for edge and building are doing. This demonstrates that statistical knowledge of the domain is a powerful tool for segmentation.