Neural Network based on Evidence Theory (NNET). This theory presents two importantPerplexity based Evidential Neural Network (PENN).Ontological PENN.Finally, we respond to the question concerning the usefulness of the low-level fusion.
information for decision-making, compared to the probabilistic methods : belief degree
and system ignorance. Then, NNET has been improved by incorporating the relationship
between descriptors and concepts, modeled by a weight vector based on entropy and perplexity.
The combination of this vector with the classi ers outputs, gives us a new model
called
We have also introduced the important topic of ontology and inter-concepts similarity
(i.e. the study of relations between the classes). Indeed, the concepts are not generally
expressed in isolation and a strong correlation exists between certain classes. The rst
diculty lies in the use of an ontology that describes the relationships between concepts.
The second concerns us more, is the operation of this semantic information. Three types of
information are used : low-level visual descriptors, co-occurrence and semantic similarities,
in conjunction with a multimedia knowledge database for semantic interpretation of video
shots. The nal system is called
This was possible only through a statistical study of data before and after features fusion.
The proposed systems have been validated on data from TRECVid (NoE K-Space project)
and soccer videos provided by Orange-France Telecom Labs (CRE- Fusion project).
Today, the access to documents in databases, archives and Internet is mainly through
textual data : image names or keywords. This search is not without faults : spelling, omission,
etc. The recent advances in the eld of image analysis and machine learning could provide
solutions such as features-based indexing and retrieval, using color, shape, texture, motion,
audio and text. These features are rich in information, especially from the semantic point
of view.
This work deals with information retrieval and aims at semantic indexing of multimedia
documents : video shots and key-frames. Indexing is an operation that consists of extracting,
representing and organizing the content of documents in a database.
However, indexation is confronted with the \semantic gap" problem between low-level
visual representations and high-level features (concepts). To limit the consequences of this
issue, we introduced into the system, di
erent types of descriptors, while taking advantage
of the scienti c advances in the eld of machine learning and the multi-level fusion. Indeed,
fusion is used to combine several heterogeneous information from multiple sources, to obtain
more complete, global and higher quality information. It can be applied to di
erent levels
of the classi cation process. Here, we studied the low-level feature fusion, high-level feature
fusion and decision fusion.
First, we present a state of the art of high-level fusion methods, in the indexing and
search systems. In particular, the adaptation of evidence theory to neural network, thus
giving
