UHigh-Level Feature Detection with Forests of Fuzzy Decision Trees combined with the RankBoost Algorithm

In this paper, we present the methodology we applied in our submission to the NIST TRECVID’2007 evaluation. We participated in the High-level Feature Extraction task. Our approach is based on the use of a Forest of Fuzzy Decision Trees combined with the RankBoost algorithm. 1 Structured Abstract Summary Here we present the contribution of the University of Paris 6 at TRECVID 2007 [6]. It concerns only the High-Level Feature Extraction task. The approach focuses on the use of Forests of Fuzzy Decision Trees (FFDT) that can be possibly combined with the RankBoost algorithm, and is based on a rather simple image description. In the following, we start with a short summary of the used method and starting from Section 3, our approach is detailed. First, we describe the particularities of our set of descriptors. Then we explain how the training (Section 4) and classification (Section 5) was performed. Before concluding, the submitted runs are discussed in details (Section 6). 1.1 Brief Description of the Submitted Run Here is the general information about the submitted run: