Multiple-Cue Object Recognition on outside datasets

This work builds upon the fact that robots can observe humans interacting with the objects in their environment, and that humans provide numerous non-visual cues to the identity of objects. In previous work, we outlined a Multiple-Cue Object Recognition (MCOR) algorithm which attempted to use multiple features of any type to produce more robust object recognition. All results so far reported with MCOR have been on data collected by ourselves. In this work, we introduce new advancements in the MCOR algorithm to increase its effectiveness and ability to deal with complex real data from outside datasets. These advancements include the integration of Scale-Invariant Feature Transform (SIFT) features and an improvement in training. To demonstrate the effectiveness of the MCOR framework, we first show a comparison of the MCOR algorithm to an outside dataset to show its basic advantages. We then demonstrate the advanced MCOR features on real television video datasets in particular cooking.