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Learning a Combined Model of Visual Saliency for Fixation Prediction.
- Source :
-
IEEE Transactions on Image Processing . Apr2016, Vol. 25 Issue 4, p1566-1579. 14p. - Publication Year :
- 2016
-
Abstract
- A large number of saliency models, each based on a different hypothesis, have been proposed over the past 20 years. In practice, while subscribing to one hypothesis or computational principle makes a model that performs well on some types of images, it hinders the general performance of a model on arbitrary images and large-scale data sets. One natural approach to improve overall saliency detection accuracy would then be fusing different types of models. In this paper, inspired by the success of late-fusion strategies in semantic analysis and multi-modal biometrics, we propose to fuse the state-of-the-art saliency models at the score level in a para-boosting learning fashion. First, saliency maps generated by several models are used as confidence scores. Then, these scores are fed into our para-boosting learner (i.e., support vector machine, adaptive boosting, or probability density estimator) to generate the final saliency map. In order to explore the strength of para-boosting learners, traditional transformation-based fusion strategies, such as Sum, Min, and Max, are also explored and compared in this paper. To further reduce the computation cost of fusing too many models, only a few of them are considered in the next step. Experimental results show that score-level fusion outperforms each individual model and can further reduce the performance gap between the current models and the human inter-observer model. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 10577149
- Volume :
- 25
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Image Processing
- Publication Type :
- Academic Journal
- Accession number :
- 113293488
- Full Text :
- https://doi.org/10.1109/TIP.2016.2522380