Multimedia semantic indexing using model vectors

In this paper we propose a novel method for multimedia semantic indexing using model vectors. Model vectors provide a semantic signature for multimedia documents by capturing the detection of concepts broadly across a lexicon using a set of independent binary classifiers. While recent techniques have been developed for detecting simple generic concepts such as indoors, outdoors, nature, manmade, faces, people, speech, music, and so forth [W.H. Adams et al., November 2002], these labels directly support only a small number of queries. Model vectors address the problem of answering queries for which relationships to specific concepts is either unknown or indirect by developing a basis across across the lexicon. In the simplest case, each model vector dimension corresponds to the confidence score by which a corresponding concept from the lexicon is detected. However, we show how other information such as relevance, reliability and concept correlation can also be incorporated. Overall, the model vectors can be used in a variety of methods for multimedia indexing, including model-based retrieval, relevance feedback searching and concept querying. In this paper, we present the model vector method and study different strategies for computing and comparing model vectors. We empirically evaluate the retrieval effectiveness of the model vector approach compared to other search methods in a large video retrieval testbed.