A Multi-feature Optimization Approach to Object-Based Image Classification

  • Qianni Zhang
  • Ebroul Izquierdo
Conference paper

DOI: 10.1007/11788034_32

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4071)
Cite this paper as:
Zhang Q., Izquierdo E. (2006) A Multi-feature Optimization Approach to Object-Based Image Classification. In: Sundaram H., Naphade M., Smith J.R., Rui Y. (eds) Image and Video Retrieval. CIVR 2006. Lecture Notes in Computer Science, vol 4071. Springer, Berlin, Heidelberg

Abstract

This paper proposes a novel approach for the construction and use of multi-feature spaces in image classification. The proposed technique combines low-level descriptors and defines suitable metrics. It aims at representing and measuring similarity between semantically meaningful objects within the defined multi-feature space. The approach finds the best linear combination of predefined visual descriptor metrics using a Multi-Objective Optimization technique. The obtained metric is then used to fuse multiple non-linear descriptors is be achieved and applied in image classification.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Qianni Zhang
    • 1
  • Ebroul Izquierdo
    • 1
  1. 1.Queen MaryUniversity of LondonLondonUK

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