Abstract
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. In this paper, we show that the Zernike moment can be used as an effective descriptor of global shape of an image in a large image database. The experimental results conducted on a database of about 6,000 images in terms of exact matching under various transformations and the similarity-based retrieval show that the proposed shape descriptor is very effective in representing shapes.
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