Improvement and invariance analysis of Zernike moments using as a region-based shape descriptor

Bin YeJia-xiong Peng

In order to retrieve an image from a large image database, the descriptor should be invariant to scale and rotation. It must also have enough discriminating power and immunity to noise for retrieval from a large image database. The Zernike moment descriptor has many desirable properties such as rotation invariance, robustness to noise, expression efficiency, fast computation and multi-level representation for describing the shapes of patterns, but it does not possess scale invariance. In this paper, we present an improved Zernike moment descriptor that not only has rotation invariance, but also has scale invariance. We apply the improved Zernike moments to image recognition using as an elective descriptor of global shape of an image in a large image database. The experimental results show that the improved Zernike moment has better invariant properties than unimproved Zernike moment using as region-based shape descriptor.

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