Unsupervised Distributed Feature Selection for Multi-view Object Recognition
Object recognition accuracy can be improved when information frommultiple views is integrated, but information in each view can oftenbe highly redundant. We consider the problem of distributed objectrecognition or indexing from multiple cameras, where thecomputational power available at each camera sensor is limited andcommunication between sensors is prohibitively expensive. In thisscenario, it is desirable to avoid sending redundant visual featuresfrom multiple views, but traditional supervised feature selectionapproaches are inapplicable as the class label is unknown at thecamera. In this paper we propose an unsupervised multi-view featureselection algorithm based on a distributed compression approach.With our method, a Gaussian Process model of the joint viewstatistics is used at the receiver to obtain a joint encoding of theviews without directly sharing information across encoders. Wedemonstrate our approach on recognition and indexing tasks withmulti-view image databases and show that our method comparesfavorably to an independent encoding of the features from eachcamera.