Integrating the Alisa Classifier with Knowledge-Based Methods for Cadastral-Map Interpretation

Summary

Alisa is a learning, statistical, texture classifier for single-and multi-class classification. Its process is based on the examination of images using a set of universal (i.e., independent of the domain of the images under examination) features. Given a set of pre-classified examples, it computes a subset of these features for a small window (i.e., the analysis token) centred at each image pixel, and creates a histogram of occurrences of the distinct feature values in the training data. After training is completed, given an unknown image Alisa examines this in the same way, and generates an isomorphic image, each pixel of which represents the normality of the corresponding pixel in the input image (or, in multi-class classification, the class in which it belongs). In this paper, we discuss the integration of Alisa with knowledge-based methods for recognising line thickness in cadastral maps.