RETRIEVING THE MOST SIMILAR SYMBOLIC PICTURES FROM PICTORIAL DATABASES

Abstract

In this article, we suggest an iconic indexing mechanism for spatial similarity retrieval on iconic image databases based upon the spatial relationships among the objects in a picture. The iconic objects we deal with are some kinds of gross panorama of simple objects. We also assume that any one iconic object is not distinguished from any other object of the same kind. For our mechanism, we first transform each iconic picture into a set of ordered triples (O(i), O(j), R(ij)) where O(i) and O(j) are objects and R(ij) is the predefined spatial relationship codes between O(i) and O(j). Then we construct a set of hashing functions for all spatial relationship codes R(ij), separately, associated with all ordered pairs (O(i), O(j)) extracted from the ordered triples (O(i), O(j), R(ij)), Thereafter, an iconic index table can be established according to the constructed hashing functions for all predefined spatial relationship codes. By applying the constructed hashing functions, the most similar pictures in the database satisfying a specified query can be fast determined. We can easily extend our mechanism for handling the case when some new spatial relationship codes are defined later for the considerations of refined spatial similarity retrieval under the maximum-likelihood measure criterion.