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A graph lattice approach to maintaining dense collections of subgraphs as image features
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Document classification and indexing methods depend on having informative image features. This paper shows how large families of complex features can be built out of simpler ones through construction of a graph lattice -- a hierarchy of related subgraphs linked in a lattice. A graph lattice enables efficiency gains that make it possible to effectively employ bag-of-words methods for document classification using high-dimensional feature vectors. Each feature is itself a subgraph, and a feature vector is a count of occurrences of subgraphs in the image. The graph lattice enables methods for adaptively growing a feature space of subgraphs tailored to observed document genres. We demonstrate the approach through classification of forms containing rectilinear line art.