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Information 2016, 7(1), 10; doi:10.3390/info7010010

A Comparative Study on Weighted Central Moment and Its Application in 2D Shape Retrieval

School of Computer Science & Engineering, Jiangsu University of Science & Technology, Zhenjiang 212003, China
School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
Author to whom correspondence should be addressed.
Academic Editor: Willy Susilo
Received: 15 December 2015 / Revised: 31 January 2016 / Accepted: 19 February 2016 / Published: 1 March 2016
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Moment invariants have been extensively studied and widely used in object recognition. The pioneering investigation of moment invariants in pattern recognition was due to Hu, where a set of moment invariants for similarity transformation were developed using the theory of algebraic invariants. This paper details a comparative analysis on several modifications of the original Hu moment invariants which are used to describe and retrieve two-dimensional (2D) shapes with a single closed contour. The main contribution of this paper is that we propose several different weighting functions to calculate the central moment according to human visual processing. The comparative results are detailed through experimental analysis. The results suggest that the moment invariants improved by weighting functions can get a better retrieval performance than the original one does. View Full-Text
Keywords: Hu moments; moment invariants; weighting function; weighted central moment Hu moments; moment invariants; weighting function; weighted central moment

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Shu, X.; Zhang, Q.; Shi, J.; Qi, Y. A Comparative Study on Weighted Central Moment and Its Application in 2D Shape Retrieval. Information 2016, 7, 10.

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