%0 Conference Proceedings
%T Pyramidal Stochastic Graphlet Embedding for Document Pattern Classification
%A Anjan Dutta
%A Pau Riba
%A Josep Llados
%A Alicia Fornes
%B 14th International Conference on Document Analysis and Recognition
%D 2017
%F Anjan Dutta2017
%O DAG; 600.097; 601.302; 600.121
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3054), last updated on Mon, 07 Dec 2020 14:27:02 +0100
%X Document    pattern    classification    methods    using graphs have received a lot of attention because of its robust representation paradigm and rich theoretical background. However, the way of preserving and the process for delineating documents with   graphs   introduce   noise   in   the   rendition   of   underlying data,  which  creates  instability  in  the  graph  representation.  To deal  with  such  unreliability  in  representation,  in  this  paper, we  propose  Pyramidal  Stochastic  Graphlet  Embedding  (PSGE).Given  a  graph  representing  a  document  pattern,  our  method first computes a graph pyramid by successively reducing the base graph. Once the graph pyramid is computed, we apply Stochastic Graphlet   Embedding   (SGE)   for   each   level   of   the   pyramid and  combine  their  embedded  representation  to  obtain  a  global delineation of the original graph. The consideration of pyramid of graphs rather than just a base graph extends the representational power  of  the  graph  embedding,  which  reduces  the  instability caused due to noise and distortion. When plugged with supportvector machine, our proposed PSGE has outperformed the state-of-the-art results in recognition of handwritten words as well as graphical  symbols
%K graph embedding
%K hierarchical graph representation
%K graph  clustering
%K stochastic  graphlet  embedding
%K graph classification
%U http://refbase.cvc.uab.es/files/DRL2017.pdf
%P 33-38