Discriminative Learing and Recognition of Image set classes Using Canonical Correlati
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28-08-2009, 02:02 AM
Discriminative Learing and Recognition of Image set classes Using Canonical Correlation
Canonical angles or principle angle between the two dimensional sub-space.
Canonical angle is compared with the two main classical methods: parametric distribution based and non parametric sample based.
The classical linear discriminate analysis which we have to develop is to be maximizing the correlation with-in the class set and it has to minimize between the class-set.
Image set after transforming the discriminate function are compared with the canonical correlation and also classical orthogonal sub-space method also compared with the proposed set.
The proposed set also used for compared with the ETH-80 database.
Each set is represented by a linear sub-space and the angle between two high-dimensional sub-spaces is exploited as a similarity measure of two sets.
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