An invariant local vector for content-based image retrieval

被引：0
|
作者：
Bigorgne, E [1 ]
Achard, C [1 ]
Devars, J [1 ]
机构：
[1] Lab Instruments & Syst, Paris, France
来源：
15TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 1, PROCEEDINGS: COMPUTER VISION AND IMAGE ANALYSIS | 2000年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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 an invariance to illuminance conditions.
引用
收藏
页码：1019 / 1022
页数：4
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