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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.
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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