Random forest with data ensemble for saliency detection

Saliency detection is one of the most active research area in computer vision. Since L. Itti et al. [1] suggested computational model of visual attention, numerous detection algorithms have been proposed. However, most of modern saliency detection methods are based on superpixels which make detectio...

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Bibliographic Details
Published in2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA) pp. 604 - 607
Main Authors Seungjun Nah, Kyoung Mu Lee
Format Conference Proceeding
LanguageEnglish
Published Asia-Pacific Signal and Information Processing Association 01.12.2015
Subjects
Computational modeling
Computer vision
Feature extraction
Image segmentation
Testing
Training
Visualization
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Summary:Saliency detection is one of the most active research area in computer vision. Since L. Itti et al. [1] suggested computational model of visual attention, numerous detection algorithms have been proposed. However, most of modern saliency detection methods are based on superpixels which make detection results have abrupt edges inside the salient part. In this paper, we propose pixel-wise detection algorithm that makes more natural detection result. It makes our algorithm excel in describing detailed part of salient objects. Furthermore, we utilize the ensemble of not only random forest but also the data itself. Our algorithm achieves comparable performance with state of the art detection results.
DOI:10.1109/APSIPA.2015.7415340