Lexicodes in the space of foot patterns for image classification

In this paper we extend work presented in Ashlock and Davidson (1997) on the automatic classification of textures with foot patterns. We begin by verifying that a technique suggested in the earlier research permits us to distinguish between textures which the original technique could not classify. We then define a metric on the space of the foot patterns and construct lexicodes of the foot patterns that yield a new technique for distinguishing the textures. The lexicodes of the foot patterns are used to construct vectors of entropy values in R/sup n/ and a clustering algorithm on those vectors is used to classify the textures. This new technique uses much of the machinery of the original technique but is unsupervised, requiring no training examples. The results of using this unsupervised technique are very similar to the results originally obtained with the supervised algorithm, including the inability to distinguish two of the six texture types in the test set. We blend the technique for distinguishing the two similar textures with the lexicode technique with partial success. We present results on binary image data but our goal is to achieve automatic classification of any gray-value texture. This has the potential to be used in automated object recognition, image retrieval from databases, and compression and data transmission applications.