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Line pattern retrieval using relational histograms

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2 Author(s)
Huet, B. ; Dept. of Comput. Sci., York Univ., UK ; Hancock, E.R.

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

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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:21 ,  Issue: 12 )