Contextual Swarm-Based Multi-layered Lattices: A New Architecture for Contextual Pattern Recognition

  • David G. Elliman
  • Sherin M. Youssef
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3163)


This paper introduces a novel approach for dynamic structuring of contextual lattices. It is anticipated that the approach can be applied to improve the accuracy of word-segmentation patterns in autonomous text recognition systems. A multi-level hierarchical structure of lattices is used to implement the algorithm, and the approach can be applied in a generic manner to other pattern recognition problems. We apply a top-down structural model in parallel with a constrained probabilistic model and intelligent distributed searching paradigm. This paradigm is based on the integration between probabilistic bi-grams and adaptive intelligent swarm-based agent search to identify the most likely sentence structures. The searching paradigm allows the exploitation of positive feedback as a search mechanism and, consequently, makes the model amenable to parallel implementation. The distributed intelligence of the proposed approach enables the dynamic structuring of contextual lattices and has proved to scale well with large lattice sizes. Moreover, we believe that the proposed architecture solves the ill-conditioned nature of most pattern recognition problems that lies in the effect of noise in the segmentation phase. To verify the developed Swarm-based Intelligent Search Algorithm (SISA), a simulation study was conducted on a set of variable size scripts. The proposed paradigm proved to be efficient in identifying the most highly segmented patterns and also returned good decisions concerning lower probability segments enabling further re- segmentations and re-combinations to take place. The paper is the first to apply the intelligent swarm-based paradigm for the identification of optimal segmented patterns in contextual recognition models. The algorithm is compared with other algorithms for the same problem, and the computational results demonstrate that the proposed approach is very efficient and robust for large-scale statistical contextual-lattice structures.


Pattern Recognition Problem Contextual Lattice Pheromone Level Segmented Word Swarm Agent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • David G. Elliman
    • 1
  • Sherin M. Youssef
    • 1
  1. 1.School of Computer Science & ITNottingham University 

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