Correspondence construction for cartoon animation via sparse coding
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Multi-view hypergraph learning by patch alignment framework
Neurocomputing
Multiple spatial pooling for visual object recognition
Neurocomputing
Quality of information-based source assessment and selection
Neurocomputing
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Recently, we have witnessed a surge of interests of learning a low-dimensional subspace for scene classification. The existing methods do not perform well since they do not consider scenes' multiple features from different views in low-dimensional subspace construction. In this paper, we describe scene images by finding a group of features and explore their complementary characteristics. We consider the problem of multiview dimensionality reduction by learning a unified low-dimensional subspace to effectively fuse these features. The new proposed method takes both intraclass and interclass geometries into consideration, as a result the discriminability is effectively preserved because it takes into account neighboring samples which have different labels. Due to the semantic gap, the fusion of multiview features still cannot achieve excellent performance of scene classification in real applications. Therefore, a user labeling procedure is introduced in our approach. Initially, a query image is provided by the user, and a group of images are retrieved by a search engine. After that, users label some images in the retrieved set as relevant or irrelevant with the query. The must-links are constructed between the relevant images, and the cannot-links are built between the irrelevant images. Finally, an alternating optimization procedure is adopted to integrate the complementary nature of different views with the user labeling information, and develop a novel multiview dimensionality reduction method for scene classification. Experiments are conducted on the real-world datasets of natural scenes and indoor scenes, and the results demonstrate that the proposed method has the best performance in scene classification. In addition, the proposed method can be applied to other classification problems. The experimental results of shape classification on Caltech 256 suggest the effectiveness of our method.