IEEE Trans Image Process - Grassmannian regularized structured multi-view embedding for image classification.

Tópicos

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Resumo

Images are usually represented by features from multiple views, e.g., color and texture. In image classification, the goal is to fuse all the multi-view features in a reasonable manner and achieve satisfactory classification performance. However, the features are often different in nature and it is nontrivial to fuse them. Particularly, some extracted features are redundant or noisy and are consequently not discriminative for classification. To alleviate these problems in an image classification context, we propose in this paper a novel multi-view embedding framework, termed as Grassmannian regularized structured multi-view embedding, or GrassReg for short. GrassReg transfers the graph Laplacian obtained from each view to a point on the Grassmann manifold and penalizes the disagreement between different views according to Grassmannian distance. Therefore, a view that is consistent with others is more important than a view that disagrees with others for learning a unified subspace for multi-view data representation. In addition, we impose the group sparsity penalty onto the low-dimensional embeddings obtained hence they can better explore the group structure of the intrinsic data distribution. Empirically, we compare GrassReg with representative multi-view algorithms and show the effectiveness of GrassReg on a number of multi-view image data sets.

Resumo Limpo

imag usual repres featur multipl view eg color textur imag classif goal fuse multiview featur reason manner achiev satisfactori classif perform howev featur often differ natur nontrivi fuse particular extract featur redund noisi consequ discrimin classif allevi problem imag classif context propos paper novel multiview embed framework term grassmannian regular structur multiview embed grassreg short grassreg transfer graph laplacian obtain view point grassmann manifold penal disagr differ view accord grassmannian distanc therefor view consist other import view disagre other learn unifi subspac multiview data represent addit impos group sparsiti penalti onto lowdimension embed obtain henc can better explor group structur intrins data distribut empir compar grassreg repres multiview algorithm show effect grassreg number multiview imag data set

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