Clustering-Based Analysis of Semantic Concept Models for Video Shots

In this paper we present a clustering-based method for representing semantic concepts on multimodal low-level feature spaces and study the evaluation of the goodness of such models with entropy-based methods. As different semantic concepts in video are most accurately represented with different features and modalities, we utilize the relative model-wise confidence values of the feature extraction techniques in weighting them automatically. The method also provides a natural way of measuring the similarity of different concepts in a multimedia lexicon. The experiments of the paper are conducted using the development set of the TRECVID 2005 corpus together with a common annotation for 39 semantic concepts.

• Type: Conference
• Year: 2006
• Where: ICMCS
• Authors: Markus Koskela, Alan F. Smeaton
• Keywords: Feature Extraction Techniques, ICMCS 2006, Multimodal Low-level Feature, Semantic Concepts