Sharing Visual Features for Multiclass and Multiview Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosting products of base classifiers
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
A novel learning approach to multiple tasks based on boosting methodology
Pattern Recognition Letters
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Image quality assessment is a challenge research topic in imaging engineering and applications, especially in the case where the reference image cannot be accessed, such as aerial images. In view of such an issue, a novel learning based evaluation approach was developed. In practice, only objective quality criteria usually cannot achieve desired result. Based on the analysis of multiple objective quality assessment criteria, a boosting algorithm with supervised learning, LassBoost (Learn to Assess with Boosting), was employed to seek the unification of the multiple objective criteria with subjective criteria. This new approach can effectively fuse multiple objective quality criteria guided by the subjective quality level such that the subjective/objective criteria can be unified using weighted regression method. The experimental results illustrate that the proposed method can achieve significantly better performance for image quality assessment, thus can provide a powerful decision support in imaging engineering and practical applications.