التفاصيل البيبلوغرافية
| العنوان: |
Learning a Combined Model of Visual Saliency for Fixation Prediction. |
| المؤلفون: |
Wang J, Borji A, Jay Kuo CC, Itti L |
| المصدر: |
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society [IEEE Trans Image Process] 2016 Apr; Vol. 25 (4), pp. 1566-79. Date of Electronic Publication: 2016 Jan 27. |
| نوع المنشور: |
Journal Article; Research Support, U.S. Gov't, Non-P.H.S. |
| اللغة: |
English |
| بيانات الدورية: |
Publisher: Institute of Electrical and Electronics Engineers Country of Publication: United States NLM ID: 9886191 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1941-0042 (Electronic) Linking ISSN: 10577149 NLM ISO Abbreviation: IEEE Trans Image Process Subsets: PubMed not MEDLINE |
| أسماء مطبوعة: |
Original Publication: New York, NY : Institute of Electrical and Electronics Engineers, 1992- |
| مستخلص: |
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. |
| تواريخ الأحداث: |
Date Created: 20160202 Date Completed: 20160720 Latest Revision: 20160226 |
| رمز التحديث: |
20231215 |
| DOI: |
10.1109/TIP.2016.2522380 |
| PMID: |
26829792 |
| قاعدة البيانات: |
MEDLINE |
Full Text Finder