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15th International Conference on Pattern Recognition (ICPR'00) - Volume 1
An Invariant Local Vector for Content-Based Image Retrieval
Barcelona, Spain
September 03-September 08
ISBN: 0-7695-0750-6
Erwan Bigorgne, Laboratoire des Instruments et Syst?mes
Catherine Achard, Laboratoire des Instruments et Syst?mes
Jean Devars, Laboratoire des Instruments et Syst?mes
In this paper, we present the use of Full-Zernike moments as a local characterization of the image signal. Their computation allows us to construct a locally invariant vector, of which the projection in an index table provides a vote for some model-image. This approach is based on the quasi-invariant theory applied to perspective transformation. Then it requires a characterization being invariant to translation, rotation and change of scale in the image; in other respect, an appropriate normalization of the signal delivers invariance to illuminance conditions.
Citation:
Erwan Bigorgne, Catherine Achard, Jean Devars, "An Invariant Local Vector for Content-Based Image Retrieval," icpr, vol. 1, pp.5019, 15th International Conference on Pattern Recognition (ICPR'00) - Volume 1, 2000
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