Feature fusion: parallel strategy vs. serial strategy

The K-L expansion method, which is able to extract the discriminatory information contained in class-mean vectors, is generalised, in this paper, to make it suitable for solving small sample size problems. We further investigate, theoretically, how to reduce the method’s computational complexity in high-dimensional cases. As a result, a simple and efficient GKLE algorithm is developed. We test our method on the ORL face image database and the NUST603 handwritten Chinese character database, and our experimental results demonstrate that GKLE outperforms the existing techniques of PCA, PCA plus LDA, and Direct LDA.

A generalised K-L expansion method which can deal with small sample size and high-dimensional problems

The problem of extracting optimal discriminant features is a critical step in image recognition. The algorithms such as classical iterative partial least squares (NIPALS and CPLS), non-iterative partial least squares based on orthogonal constraints (NIPLS), and partial least squares based on conjugation orthogonal constraints (COPLS) are introduced briefly. NIPLS and COPLS methods based on original image matrices are discussed where image covariance matrix is constructed directly using the original image matrices just like 2DPCA and 2DCCA. We call them 2DNIPLS and 2DCOPLS in the paper. Two arbitrary optimal discriminant features can be extracted by 2DCOPLS based on uncorrelated score constraints in theory. At the same time, it is pointed out that 2DCOPLS algorithm is more complicated than other PLS based algorithms. The results of experiments on ORL face database, Yale face database, and partial FERET face sub-database show that the 2DPLS algorithms presented are efficient and robust.

Two-Dimensional Partial Least Squares and Its Application in Image Recognition

An approach for solving uncorrelated optimal discriminant vectors (UODV), called indirect uncorrelated linear discriminant analysis(IULDA), is proposed. This is done by establishing a relation between canonical correlation analysis (CCA) and Fisher linear discriminant analysis(FLDA). The advantages of our method for solving the UODV over the two existing methods are analyzed theoretically. Experimental result based on the Concordia University CENPARMI handwritten character database has shown that our algorithm can increase the recognition rate and the speed of feature extraction.

An indirect and efficient approach for solving uncorrelated optimal discriminant vectors

In this paper important theories are developed based on the fact that the Fisher discriminant criterion function is a generalized Rayleigh quotient in essence. The extremum properties of generalized Rayleigh quotient are first analyzed and it is pointed out that it is rational to use conjugate orthogonal constraints instead of orthogonal constraints. Then the problem of finding the optimal discriminant vectors subjected to such constraints is solved using the property that there exist a set of conjugate orthogonal eigenvectors satisfying the generalized eigen-equation. Furthermore, it is pointed out that the theories in this paper is a progress of classical Fisher linear discriminant, and the optimal discriminant vectors presented are better than Foley-Sammons' in a sense. Furthermore, how to calculate optimal discriminant vectors in case of the within-class scatter matrix being singular is discussed and a simple approximate algorithm is given. The results of experiments on Concordia University CENPARMI handwriting numeral database and Olivetti Research Laboratory (ORL) face database show that the algorithms presented are efficient and robust.

A feature extraction approach using optimal discriminant transform and image recognition

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A feature extraction approach using optimal discriminant transform and image recognition