Shape Similarity Measure Based on Correspondence of Visual Parts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
An RKHS for multi-view learning and manifold co-regularization
Proceedings of the 25th international conference on Machine learning
Unified video annotation via multigraph learning
IEEE Transactions on Circuits and Systems for Video Technology
Multiview Metric Learning with Global Consistency and Local Smoothness
ACM Transactions on Intelligent Systems and Technology (TIST)
Heterogeneous image feature integration via multi-modal spectral clustering
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Mining coherent subgraphs in multi-layer graphs with edge labels
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Many real-world objects described by multiple attributes or features can be decomposed as multiple "views" (e.g., an image can be described by a color view or a shape view), which often provides complementary information to each other. Learning a metric (similarity measures) for multi-view data is primary due to its wide applications in practices. However, leveraging multi-view information to produce a good metric is a great challenge and existing techniques are concerned with pairwise similarities, leading to undesirable fusion metric and high computational complexity. In this paper, we propose a novel Metric Fusion technique via cross-view graph Random Walk, named MFRW, regarding a multi-view based similarity graphs (with each similarity graph constructed under each view). Instead of using pairwise similarities, we seek a high-order metric yielded by graph random walks over constructed similarity graphs. Observing that ``outlier views" may exist in the fusion process, we incorporate the coefficient matrices representing the correlation strength between any two views into MFRW, named WMFRW. The principle of \textsf{WMFRW} is implemented by exploring the ``common latent structure" between views. The empirical studies conducted on real-world databases demonstrate that our approach outperforms the state-of-the-art competitors in terms of effectiveness and efficiency.