572 research outputs found
Generalized Two-Dimensional Quaternion Principal Component Analysis with Weighting for Color Image Recognition
A generalized two-dimensional quaternion principal component analysis
(G2DQPCA) approach with weighting is presented for color image analysis. As a
general framework of 2DQPCA, G2DQPCA is flexible to adapt different constraints
or requirements by imposing norms both on the constraint function and
the objective function. The gradient operator of quaternion vector functions is
redefined by the structure-preserving gradient operator of real vector
function. Under the framework of minorization-maximization (MM), an iterative
algorithm is developed to obtain the optimal closed-form solution of G2DQPCA.
The projection vectors generated by the deflating scheme are required to be
orthogonal to each other. A weighting matrix is defined to magnify the effect
of main features. The weighted projection bases remain the accuracy of face
recognition unchanged or moving in a tight range as the number of features
increases. The numerical results based on the real face databases validate that
the newly proposed method performs better than the state-of-the-art algorithms.Comment: 15 pages, 15 figure
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