A shape descriptor based on circular hidden Markov model

Given the shape information of an object, can we find visually meaningful “n” objects in an image database, which is ranked from the most similar to the n^th similar one? The answer to this question depends on the complexity of the images in the database and the complexity of the objects in the query. This study presents a robust shape descriptor, which compares a given object to the objects in an image database and identifies “n” shapes, ranked from the most similar to the least similar one, in the database. The intended shape descriptor is based on the circular hidden Markov model (HMM) proposed by the authors (1999) of this study. The circular HMM is both ergodic and temporal. It is insensitive to size changes. Since it has no starting and terminating state, it is insensitive to the starting point of the shape boundary. The experiments, performed on 100 test shapes, indicate excellent result