Validity-weighted model vector-based retrieval of video

Model vector-based retrieval is a novel approach for video indexing that uses a semantic model vector signature that describes the detection of a fixed set of concepts across a lexicon. The model vector basis is created using a set of independent binary classifiers that correspond to the semantic concepts. The model vectors are created by applying the binary detectors to video content and measuring the confidence of detection. Once the model vectors are extracted, simple techniques can be used for searching to find similar matches in a video database. However, since confidence scores alone do not capture information about the reliability of the underlying detectors, techniques are needed to ensure good performance in the presence of varying qualities of detectors. In this paper, we examine the model vector-based retrieval framework for video and propose methods using detector validity to improve matching performance. In particular, we develop a model vector distance metric that weighs the dimensions using detector validity scores. In this paper, we explore the new model vector-based retrieval method for video indexing and empirically evaluate the retrieval effectiveness on a large video test collection using different methods of measuring and incorporating detector validity indicators.