Exploring the consensus and complementary information over visual data objects from multiple views

More often than not, visual data objects, such as images, can be described by multiplefeatures due to its multi-view nature, with each view corresponding to an individualfeature type. For example, an image can be described by a color view and a shape view.Commonly, these views provide complementary information to each other, which canlead to superior performance over single view based models by combining heterogeneousviews. One critical challenge of successfully leveraging the complementary information liesin how to explore the consensus among all views. To address such challenge, we proposea series of novel methods to achieve the multi-view consensus so that the complementaryinformation from multiple views can be seamlessly leveraged. Specifically, we propose toleverage pair-wise similarity metric as well as high order similarity from multiple viewsfor visual objects retrieval, semi-supervised based manifold ranking and multi-view basedhashing similarity search over large scale visual data objects. To show the significance ofour techniques, we further apply them to applications such as clustering, mode seekingand salience detection. We experimentally validate the effectiveness of our proposed multi-view basedmethods, which can well achieve the multi-view consensus on both synthetic and real-worlddatasets. It demonstrates that our proposed methods outperform both existing multi-view and single view based methods