Layouts and sub-layouts constitute an important clue while searching a document on the basis of its structure, or when textual content is unknown irrelevant. A sub-layout specifies the arrangement of document entities within a smaller portion of the document. We propose an efficient graph-based matching algorithm, integrated with hash-based indexing, to prune a possibly large search space. A user can specify a combination of sub-layouts of interest using sketch-based queries. The system supports partial matching for unspecified layout entities. We handle cases of segmentation pre-processing errors (for text non-text blocks) with a symmetry maximization-based strategy, and accounting for multiple domain-specific plausible segmentation hypotheses. We show promising results of our system on a database of unstructured entities, containing 4776 newspaper images.
@cite_7 present a graph-based method for document retrieval, which can perform sub-graph search and spotting. The authors start from constructed graphs, and do not consider segmentation of images and obtaining the subsequent graph structure. The paper mentions that the overall accuracy depends on the formation of the graph structure.
@cite_7
2017844419
Structural pattern recognition approaches offer the most expressive, convenient, powerful but computational expensive representations of underlying relational information. To benefit from mature, less expensive and efficient state-of-the-art machine learning models of statistical pattern recognition they must be mapped to a low-dimensional vector space. Our method of explicit graph embedding bridges the gap between structural and statistical pattern recognition. We extract the topological, structural and attribute information from a graph and encode numeric details by fuzzy histograms and symbolic details by crisp histograms. The histograms are concatenated to achieve a simple and straightforward embedding of graph into a low-dimensional numeric feature vector. Experimentation on standard public graph datasets shows that our method outperforms the state-of-the-art methods of graph embedding for richly attributed graphs. Highlights? We propose an explicit graph embedding method. ? We perform multilevel analysis of graph to extract global, topological structural and attribute information. ? We use homogeneity of subgraphs in graph for extracting topological structural details. ? We encode numeric information by fuzzy histograms and symbolic information by crisp histograms. ? Our method outperforms graph embedding methods for richly attributed graphs.