Meta-classification: Combining Multimodal Classifiers

Combining multiple classifiers is of particular interest in multimedia applications. Each modality in multimedia data can be analyzed individually, and combining multiple pieces of evidence can usually improve classification accuracy. However, most combination strategies used in previous studies implement some ad hoc designs, and ignore the varying expertise of specialized individual modality classifiers in recognizing a category under particular circumstances. In this paper we present a combination framework called meta-classification, which models the problem of combining classifiers as a classification problem itself. We apply the technique on a wearable experience collection system, which unobtrusively records the wearer's conversation, recognizes the face of the dialogue partner, and remember his/her voice. When the system sees the same person's face or hears the same voice, it can then use a of the last conversation to remind the wearer. To identify a person correctly from a mixture of audio and video streams, classification judgments from multiple modalities must be effectively combined. Experimental results show that combining different face recognizers and speaker identification aspects using the meta-classification strategy can dramatically improve classification accuracy, and is more effective than a fixed probability-based strategy. Other work in labeling weather news broadcasts showed that meta-classification is a general framework that can be applied to any application that needs to combine multiple classifiers without much modification.