A supervised nonlinear neighborhood embedding of color histogram for image indexing

Subspace learning techniques are widespread in pattern recognition research. They include PCA, ICA, LPP, etc. These techniques are generally linear and unsupervised. The problem of image indexing is very complicated and the processed images are usually lie on non-linear image subspaces. In this paper, we propose a supervised nonlinear neighborhood embedding algorithm which learns an adaptive nonlinear subspace by preserving the neighborhood structure of the image color space. In the proposed algorithm, we combine the idea of nonlinear kernel mapping and preserving the neighborhood structure of the samples, so it can not only gain a perfect approximation of the nonlinear image manifold, but also enhance within-class neighborhood information. Experimental results show that the proposed method outperform other linear or unsupervised subspace learning methods.