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Quaternion Exponent Moments and Their Invariants for Color Image

Yang H.-Y., Liang L.-L., Li Y.-W., Wang X.-Y.

Fundamenta Informaticae

2016

Vol. 145, nr 2

189--205

Moments and moment invariants have become a powerful tool in image processing owing to their image description capability and invariance property. But, conventional methods are mainly introduced to deal with the binary or gray-scale images, and the only approaches for color image always have poor color image description capability. Based on Exponent moments (EMs) and quaternion, we introduced the quaternion Exponent moments (QEMs) for describing color images in this paper, which can be seen as the generalization of EMs for gray-level images. It is shown that the QEMs can be obtained from the EMs of each color channel. We derived and analyzed the rotation, scaling, and translation (RST) invariant property of QEMs. We also discussed the problem of color image retrieval using QEMs. Experimental results are provided to illustrate the efficiency of the proposed color image descriptors.

Weighted pseudo-metric for a fast CBIR method

Ksantini R., Ziou D., Colin B., Dubeau F.

Machine Graphics and Vision

2006

Vol. 15, No. 3/4

471-480

In this paper, a simple and fast querying method for content-based image retrieval is presented. In order to measure the similarity degree between two color images both quickly and effectively, we use a weighted pseudo-metric employing one-dimensional Daubechies decomposition and compression of the extracted feature vectors. In order to improve the discriminatory capacity of the pseudo-metric, we compute its weights using separately a classical logistic regression model and a Bayesian logistic regression model. The Bayesian logistic regression model was shown to be significantly better than the classical logistic regression model at improving the retrieval performance. Experimental results are reported on the WANG and ZuBuD color image databases proposed by [Deselaers T., Keysers D., Ney H.: Classification error rate for quantitative evaluation of content-based image retrieval systems. 17th International Conference on Pattern Recognition (ICPR'04), 2, pp. 505-508, Cambridge, UK].