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

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• 作者：Xiaoliu Chen ; Imran Shafiq Ahmad
• 关键词：image retrieval ; shape ; based image retrieval ; k ; means clustering ; moment ; invariants ; Zernike moments
• 刊名：Lecture Notes in Computer Science
• 出版年：2007
• 出版时间：2007
• 年：2007
• 卷：4872
• 期：1
• 页码：893-904
• 全文大小：459.5 KB
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

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.