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Line Pattern Retrieval Using Relational Histograms
December 1999 (vol. 21 no. 12)
pp. 1363-1370

Abstract—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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Index Terms:
Image database, line patterns, content-based retrieval, relational representation, geometric features, histogram comparison.
Citation:
Benoit Huet, Edwin R. Hancock, "Line Pattern Retrieval Using Relational Histograms," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 12, pp. 1363-1370, Dec. 1999, doi:10.1109/34.817414
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