Paper Info
Title
Random forest with data ensemble for saliency detection.
Abstract
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.
Year
Venue
Field
2015
Asia-Pacific Signal and Information Processing Association Annual Summit and Conference
Computer vision,Object-class detection,Kadir–Brady saliency detector,Feature detection (computer vision),Pattern recognition,Edge detection,Computer science,Salience (neuroscience),Feature extraction,Image segmentation,Artificial intelligence,Random forest
DocType
ISSN
Citations 
Conference
2309-9402
0
PageRank 
References 
Authors
0.34
9
2
Name
Order
Citations
PageRank
Seungjun Nah140612.44
Kyoung Mu Lee23228153.84