TY  - CONF
AU  - Anjan Dutta
AU  - Pau Riba
AU  - Josep Llados
AU  - Alicia Fornes
A2  - ICDAR
PY  - 2017//
TI  - Pyramidal Stochastic Graphlet Embedding for Document Pattern Classification
BT  - 14th International Conference on Document Analysis and Recognition
SP  - 33
EP  - 38
KW  - graph embedding
KW  - hierarchical graph representation
KW  - graph  clustering
KW  - stochastic  graphlet  embedding
KW  - graph classification
N2  - 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
L1  - http://refbase.cvc.uab.es/files/DRL2017.pdf
N1  - DAG; 600.097; 601.302; 600.121
ID  - Anjan Dutta2017
ER  -