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Salient object detection: A discriminative regional feature integration approach (2013)
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| Venue: | In CVPR |
| Citations: | 27 - 4 self |
BibTeX
@INPROCEEDINGS{Jiang13salientobject,
author = {Huaizu Jiang and Jingdong Wang and Zejian Yuan and Yang Wu and Nanning Zheng and Shipeng Li},
title = {Salient object detection: A discriminative regional feature integration approach},
booktitle = {In CVPR},
year = {2013}
}
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Abstract
Salient object detection has been attracting a lot of interest, and recently various heuristic computational models have been designed. In this paper, we regard saliency map computation as a regression problem. Our method, which is based on multi-level image segmenta-tion, uses the supervised learning approach to map the regional feature vector to a saliency score, and finally fuses the saliency scores across multiple levels, yielding the saliency map. The contributions lie in two-fold. One is that we show our approach, which integrates the regional contrast, regional property and regional backgroundness descriptors together to form the master saliency map, is able to produce superior saliency maps to existing algorithms most of which combine saliency maps heuristically computed from different types of fea-tures. The other is that we introduce a new regional fea-ture vector, backgroundness, to characterize the back-ground, which can be regarded as a counterpart of the objectness descriptor [2]. The performance evaluation on several popular benchmark data sets validates that our approach outperforms existing state-of-the-arts. 1.
Keyphrases
salient object detection discriminative regional feature integration approach saliency map regional backgroundness descriptor regional property various heuristic computational model regional contrast objectness descriptor multi-level image segmenta-tion multiple level regression problem master saliency map supervised learning approach new regional fea-ture vector superior saliency map saliency score performance evaluation different type regional feature vector several popular benchmark data set saliency map computation
