Image Classification Using Group Sparse Multiview Patch Alignment Framework Method

We cannot classifying the images using Single feature. Multiview learning aims to unify different kinds of features to produce an efficient representation. This technique redefines part optimization in the patch alignment framework (PAF) and develops a group sparse multiview patch alignment framework (GSM-PAF). The new part optimization considers not only the complementary properties of different views, but also views consistency. In particular, view consistency models the correlations between all possible combinations of any two kinds of view. In contrast to conventional dimensionality reduction algorithms that perform feature extraction and feature selection independently, GSM-PAF enjoys joint feature extraction and feature selection which leads to the simultaneous selection of relevant features and learning transformation, and thus makes the algorithm more discriminative.