Abstract
Local descriptors have been one of the most intensively examined mechanisms of image analysis. In this paper, we propose a new chain code-based local descriptor. Unlike many other descriptors existing in the literature, this descriptor is based on string values, which are obtained when starting from a particular point of the image and searching for extrema in a given neighborhood and memorizing a path being traversed through the consequent pixels of the image. We demonstrate that this approach is efficient and helps us preserve both local and global properties of the object. To compare the words we apply the Levenshtein distance. Moreover, four similarity measures (correlation, histogram intersection, chi-square, and Hellinger) are used to compare the histograms of words in the process of classification.