Low-level feature fusion models for soccer scene classification

This paper presents an automatic semantic concept extraction method which employs low level visual feature fusion. Both static and dynamic feature fusion approaches are studied and evaluated. The main contributions of this paper are: a novel dynamic feature fusion approach inspired from coding is proposed to create compact yet rich signatures; Statistical study of descriptors with and without fusion. To validate and evaluate our approach, we have conducted a set experiments on the classification of soccer video shots. These experiments show, in particular, that the feature fusion step of our system increases the classification rate of 17% comparing to a system without feature fusion.