Image Analysis Using Hahn Moments

Yap, Pew-Thian; Raveendran, P.; Ong, S. H.

doi:10.1109/tpami.2007.70709

Cited by 196 publications

(90 citation statements)

References 7 publications

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“…Classical methods include moment invariants [15] , [16], Fourier transform coefficients [17] , [18], edge curvature and arc length [7]. In the MPEG-7 standard, three shape descriptors are used for object-based image retrieval: 3-D shape descriptor, region-based shape derived from Zernik moments and curvature scale space (CSS) descriptor [10].…”

Section: Related Workmentioning

confidence: 99%

Image retrieval based on micro-structure descriptor

Liu

Zhang

et al. 2011

Pattern Recognition

299

203

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a b s t r a c tThis paper presents a simple yet efficient image retrieval approach by proposing a new image feature detector and descriptor, namely the micro-structure descriptor (MSD). The micro-structures are defined based on an edge orientation similarity, and the MSD is built based on the underlying colors in microstructures with similar edge orientation. With micro-structures serving as a bridge, the MSD extracts features by simulating human early visual processing and it effectively integrates color, texture, shape and color layout information as a whole for image retrieval. The proposed MSD algorithm has high indexing performance and low dimensionality. Specifically, it has only 72 dimensions for full color images, and hence it is very efficient for image retrieval. The proposed method is extensively tested on Corel datasets with 15,000 natural images. The results demonstrate that it is much more efficient and effective than representative feature descriptors, such as Gabor features and multi-textons histogram, for image retrieval.Crown

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Section: Related Workmentioning

confidence: 99%

Image retrieval based on micro-structure descriptor

Liu

Zhang

et al. 2011

Pattern Recognition

299

203

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“…To overcome the shortcomings of numerical fluctuations, scaled Hahn polynomials are adopted frequently (Yap et al, 2007),…”

Section: Bivariate Hahn Polynomials and Momentsmentioning

confidence: 99%

Bivariate Hahn moments for image reconstruction

Wu¹,

Yan

2014

International Journal of Applied Mathematics and Computer Science

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This paper presents a new set of bivariate discrete orthogonal moments which are based on bivariate Hahn polynomials with non-separable basis. The polynomials are scaled to ensure numerical stability. Their computational aspects are discussed in detail. The principle of parameter selection is established by analyzing several plots of polynomials with different kinds of parameters. Appropriate parameters of binary images and a grayscale image are obtained through experimental results. The performance of the proposed moments in describing images is investigated through several image reconstruction experiments, including noisy and noise-free conditions. Comparisons with existing discrete orthogonal moments are also presented. The experimental results show that the proposed moments outperform slightly separable Hahn moments for higher orders.

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“…Shape is one of the most basic and meaningful characteristics, shape descriptors should be invariant to translation, rotation and scaling changes of the object on the basis of distinguishing different objects. Moment has been widely used in pattern recognition applications to describe the geometrical characteristics of different objects, its calculation is related to all of the relevant pixels of the image or region, it can describe the global information, and Krawtchouk moment [16] proposed by Yap is a set of discrete orthogonal moments, there is no need for spatial normalization, hence the error in the computed Krawtchouk moments due to discretization is nonexistent, and it has smaller redundancy than other moments, at the same time Krawtchouk moment invariants is invariant to rotation, scaling, and translation of the image. Krawtchouk moment invariants as global shape features achieve image retrieval.…”

Section: Introductionmentioning

confidence: 99%

Image Retrieval by Using Non-subsampled Shearlet Transform and Krawtchouk Moment Invariants

Wan

2015

Computer Vision - ACCV 2014 Workshops

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Abstract. In this paper, we use non-subsampled shearlet transform (NSST) and Krawtchouk Moment Invariants (KMI) to realize image retrieval based on texture and shape features. Shearlet is a new sparse representation tool of multidimensional function, which provides a simple and efficient mathematical framework. We decompose the images by NSST. The directional subband coefficients are modeled by Generalized Gaussian Distribution (GGD). The distribution parameters are used to build texture feature vectorswhich are measured by Kullback-Leibler distance (KLD). Meanwhile, low-order KMI are employed to extract shape features which are measured by Euclidean distance (ED). Finally, the image retrieval is achieved based on weighted distance measurement. Experimental results show the proposed retrieval system can obtain the highest retrieval rate comparing with the methods based on DWT, Contourlet, NSCT and DT-CWT.

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Image Analysis Using Hahn Moments

Cited by 196 publications

References 7 publications

Image retrieval based on micro-structure descriptor

Image retrieval based on micro-structure descriptor

Bivariate Hahn moments for image reconstruction

Image Retrieval by Using Non-subsampled Shearlet Transform and Krawtchouk Moment Invariants

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