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Shape-Based Image Retrieval Using k-Means Clustering and Neural Networks

  • Xiaoliu Chen
  • Imran Shafiq Ahmad
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4872)

Abstract

Shape is a fundamental image feature and belongs to one of the most important image features used in Content-Based Image Retrieval. This feature alone provides capability to recognize objects and retrieve similar images on the basis of their contents. In this paper, we propose a neural network-based shape retrieval system in which moment invariants and Zernike moments are used to form a feature vector. k-means clustering is used to group correlated and similar images in an image collection into k disjoint clusters whereas neural network is used as a retrieval engine to measure the overall similarity between the query and the candidate images. The neural network in our scheme serves as a classifier such that the moments are input to it and its output is one of the k clusters that has the largest similarity to the query image.

Keywords

image retrieval shape-based image retrieval k-means clustering moment-invariants Zernike moments 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Xiaoliu Chen
    • 1
  • Imran Shafiq Ahmad
    • 1
  1. 1.School of Computer Science, University of Windsor, Windsor, ON N9B 3P4Canada

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