Salient Object Detection for Searched Web Images via Global Saliency
Abstract
In this paper, we deal with the problem of detecting the existence and the location of salient objects for thumbnail images on which most search engines usually perform visual analysis in order to handle web-scale images. Different from previous techniques, such as sliding window-based or segmentation-based schemes for detecting salient objects, we propose to use a learning approach, random forest in our solution. Our algorithm exploits global features from multiple saliency indicators to directly predict the existence and the position of the salient object. To validate our algorithm, we constructed a large image database collected from Bing image search, that contains hundreds of thousands of manually labeled web images. The experimental results using this new database and the resized MSRA database [16] demonstrate that our algorithm outperforms previous state-of-the-art methods.
Cite
Text
Wang et al. "Salient Object Detection for Searched Web Images via Global Saliency." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2012. doi:10.1109/CVPR.2012.6248054Markdown
[Wang et al. "Salient Object Detection for Searched Web Images via Global Saliency." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2012.](https://mlanthology.org/cvpr/2012/wang2012cvpr-salient/) doi:10.1109/CVPR.2012.6248054BibTeX
@inproceedings{wang2012cvpr-salient,
title = {{Salient Object Detection for Searched Web Images via Global Saliency}},
author = {Wang, Peng and Wang, Jingdong and Zeng, Gang and Feng, Jie and Zha, Hongbin and Li, Shipeng},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2012},
pages = {3194-3201},
doi = {10.1109/CVPR.2012.6248054},
url = {https://mlanthology.org/cvpr/2012/wang2012cvpr-salient/}
}