Abstract
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.
Highlights
Shape is a significant visual clue for human perception
Many other kinds of moments have been proposed in the literature, including Zernike moments [3,4,5], Legendre moments [6,7], Fourier-Mellin moments [8,9,10], etc. [2]
It is clear from the average precision and recall curves that the modified Hu moment invariants based on the linear weighting function, non-linear weighting function, balance weighting function and central weighting function can get better retrieval results compared with the original Hu moment invariants and the boundary moment invariants
Summary
Shape is a significant visual clue for human perception. Using the shape of an object for object recognition and image retrieval is a hot topic in computer vision [1]. The Fourier-Mellin moment is one of the complex moments and it was proposed by Sheng and Shen [12]; it can be transformed to rotation and translation invariants It attains good results in shape recognition. These various moment invariants have been successfully utilized as pattern features in a number of applications including character recognition [13,14], aircraft recognition [15], object identification and discrimination [16,17], content-based image retrieval [18], two-dimensional (2D) flow fields analysis [19], etc. Introduced the curve moment invariants, which are reformulations of Hu's moments, and they are a set of invariants devised in such a way as to be evaluated only with the object boundary pixels Though this modification reduces computation, the shape information is reduced to a certain extent as well.
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