Multi-instance multi-label learning (MIML) refers to the
learning problems where each example is represented by a
bag/collection of instances and is labeled by multiple labels.
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Rong Jin (Michigan State University), Shijun Wang...
A new paradigm for the efficient color-based tracking of objects seen from a moving camera is presented. The proposed technique employs the mean shift analysis to derive the targe...
In this paper, we present an approach to incorporating partial geometric information into a local feature-based The distance-supported shape index is proposed for the representatio...
This paper reports on a new technique for unconstrained license plate detection in a surveillance context. The proposed algorithm quickly finds license plates by performing the fo...
We present an efficient method for learning part-based object class models from unsegmented images represented as sets of salient features. A model includes parts' appearance...