Audio-Visual Fusion for Film Database Retrieval and Classification

Abstract

This chapter presents the techniques for the characterization and fusion of audio and visual content in videos, and demonstrates their applications in movie database retrieval. In the audio domain, a study is conducted on the peaky nature of the distribution of wavelet coefficients of an audio signal, which cannot be effectively modeled by a single distribution. Thus, a new modeling method based on a Laplacian mixture model is studied for analyzing audio content and extracting audio features. The dimension of the indexed features is low, which is important for the retrieval efficiency of the system in terms of response time. Together with the audio feature, the visual feature is extracted by template frequency modeling. Both features are referred to as perceptual features. Then, a learning algorithm for audiovisual fusion is presented. Specifically, the two features are fused at the late fusion stage and input into a support vector machine to learn semantic concepts from a given video database. Based on the experimental results, the current system implementing the support vector machine-based fusion technique achieves high classification accuracy when applied to a large volume database containing Hollywood movies.