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Multi-dimensional BPTs for Content Retrieval

  • Shirin Ghanbari
  • John C. Woods
  • Simon M. Lucas
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 231)

Abstract

This chapter documents the use of region image based analysis within binary partition trees to produce meaningful segmentations suitable for MPEG-7 description. An image is pre-segmented into a large number of homogenous regions which are subsequently merged according to their similarity with the process documented using a binary partition tree. The trees are derived using multi-dimensional descriptors such as colour, and texture. The correlations between these domains are studied leading to a tree where objects are constrained to individual branches rather than being fragmented. The tree is then pruned to retain meaningful areas leading to high density semantic image segments that are highly valuable in other applications such as in content retrieval systems. Object nodes generated from the BPT are indexed and matched through a combination of descriptors. Results justify the use of segmentation within retrieval systems. Based on MPEG-7 colour descriptors of colour, texture and edge histograms, a semi-automatic segment based image retrieval is studied. 

Keywords

Image Segmentation Image Retrieval Query Image Object Node Binary Partition 
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 2009

Authors and Affiliations

  • Shirin Ghanbari
    • 1
  • John C. Woods
    • 1
  • Simon M. Lucas
    • 1
  1. 1.School of Computer Science and Electronic EngineeringUniversity of EssexUK

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