A Graph Theoretic Approach for Object Shape Representation in Compositional Hierarchies Using a Hybrid Generative-Descriptive Model

  • Umit Rusen Aktas
  • Mete Ozay
  • Aleš Leonardis
  • Jeremy L. Wyatt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8691)

Abstract

A graph theoretic approach is proposed for object shape representation in a hierarchical compositional architecture called Compositional Hierarchy of Parts (CHOP). In the proposed approach, vocabulary learning is performed using a hybrid generative-descriptive model. First, statistical relationships between parts are learned using a Minimum Conditional Entropy Clustering algorithm. Then, selection of descriptive parts is defined as a frequent subgraph discovery problem, and solved using a Minimum Description Length (MDL) principle. Finally, part compositions are constructed using learned statistical relationships between parts and their description lengths. Shape representation and computational complexity properties of the proposed approach and algorithms are examined using six benchmark two-dimensional shape image datasets. Experiments show that CHOP can employ part shareability and indexing mechanisms for fast inference of part compositions using learned shape vocabularies. Additionally, CHOP provides better shape retrieval performance than the state-of-the-art shape retrieval methods.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Umit Rusen Aktas
    • 1
  • Mete Ozay
    • 1
  • Aleš Leonardis
    • 1
  • Jeremy L. Wyatt
    • 1
  1. 1.School of Computer ScienceThe University of BirminghamBirminghamUK

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