The nature of statistical learning theory
The nature of statistical learning theory
Efficient use of local edge histogram descriptor
MULTIMEDIA '00 Proceedings of the 2000 ACM workshops on Multimedia
Toward a unified approach to statistical language modeling for Chinese
ACM Transactions on Asian Language Information Processing (TALIP)
Introduction to MPEG-7: Multimedia Content Description Interface
Introduction to MPEG-7: Multimedia Content Description Interface
Class distribution on SOM surfaces for feature extraction and object retrieval
Neural Networks - 2004 Special issue: New developments in self-organizing systems
MMM'07 Proceedings of the 13th International conference on Multimedia Modeling - Volume Part II
Classifier fusion: combination methods for semantic indexing in video content
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Portfolio theory of multimedia fusion
Proceedings of the international conference on Multimedia
A new content-based image retrieval technique using color and texture information
Computers and Electrical Engineering
Content-based image retrieval by integrating color and texture features
Multimedia Tools and Applications
| Hi-index | 0.00 |
In this paper, an automatic content-based video shot indexing framework is proposed employing five types of MPEG-7 low-level visual features (color, texture, shape, motion and face). Once the set of features representing the video content is determined, the question of how to combine their individual classifier outputs according to each feature to form a final semantic decision of the shot must be addressed, in the goal of bridging the semantic gap between the low level visual feature and the high level semantic concepts. For this aim, a novel approach called "perplexity-based weighted descriptors" is proposed before applying our evidential combiner NNET [3], to obtain an adaptive classifier fusion PENN (Perplexity-based Evidential Neural Network). The experimental results conducted in the framework of the TRECVid'07 high level features extraction task report the efficiency and the improvement provided by the proposed scheme.