Line pattern retrieval using relational histograms

This paper presents a new compact shape representation for retrieving line-patterns from large databases. The basic idea is to exploit both geometric attributes and structural information to construct a shape histogram. We realize. this goal by computing the N-nearest neighbor graph for the lines-segments for: each pattern. The edges of the neighborhood graphs are used to gate contributions to a two-dimensional pairwise geometric histogram. Shapes are indexed by searching for the line-pattern that maximizes the cross correlation of the normalized histogram bin-contents. We evaluate the new method on a database containing over 2,500 line-patterns each composed of hundreds of lines.