A Similarity-Based Approach for Audiovisual Document Classification Using Temporal Relation Analysis

We propose a novel approach for video classification that bases on the analysis of the temporal relationships between the basic events in audiovisual documents. Starting from basic segmentation results, we define a new representation method that is called Temporal Relation Matrix (TRM). Each document is then described by a set of TRMs, the analysis of which makes events of a higher level stand out. This representation has been first designed to analyze any audiovisual document in order to find events that may well characterize its content and its structure. The aim of this work is to use this representation to compute a similarity measure between two documents. Approaches for audiovisual documents classification are presented and discussed. Experimentations are done on a set of 242 video documents and the results show the efficiency of our proposals.