In statistical pattern recognition, parameters of distributions are usually estimated from training samples. It is well known that shortage of training samples causes estimation e...
We present a method to learn object class models from unlabeled and unsegmented cluttered scenes for the purpose of visual object recognition. We focus on a particular type of mode...
We try to link elastic matching with a statistical discrimination framework to overcome the overfitting problem which often degrades the performance of elastic matchingbased onli...
This paper explores the use of alternating sequential patterns of local features and saccading actions to learn robust and compact object representations. The temporal encoding rep...
In this paper we introduce a new type of pattern – a flipping correlation pattern. The flipping patterns are obtained from contrasting the correlations between items at diffe...
Marina Barsky, Sangkyum Kim, Tim Weninger, Jiawei ...