Multi-level fusion for semantic video content indexing and retrieval

In this paper, we present the results of our work on the analysis of
an automatic semantic video content indexing and retrieval system based on fusing
various low level visual and edges descriptors. Global MPEG-7 features, extracted
from video shots, are described via IVSM signature (Image Vector Space
Model) in order to have a compact description of the content. Both static and dynamic
feature fusion are introduced to obtain effective signatures. Support Vector
Machines (SVMs) are employed to perform classification (One classifier per
feature). The task of the classifiers is to detect the video semantic content. Then,
classifier outputs are fused using a neural network based on evidence theory (NNET)
in order to provide a decision on the content of each shot. The experimental
results are conducted in the framework of the TrecVid feature extraction task.