Shape-based image retrieval using k-means clustering and neural networks

Authors:
Xiaoliu Chen;Imran Shafiq Ahmad
Affiliations:
School of Computer Science, University of Windsor, Windsor, ON, Canada;School of Computer Science, University of Windsor, Windsor, ON, Canada
Venue:
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
Year:
2007

Citing 4
Cited 1

Fast algorithm for the computation of moment invariants

Pattern Recognition
On Image Analysis by the Methods of Moments

IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital image processing algorithms

Digital image processing algorithms
The Amsterdam Library of Object Images

International Journal of Computer Vision

Shape-based image retrieval

Proceedings of the 7th International Conference on Advances in Mobile Computing and Multimedia

Quantified Score

Hi-index	0.00

Visualization

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. kmeans 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.