A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contrast-based image attention analysis by using fuzzy growing
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Salient Closed Boundary Extraction with Ratio Contour
IEEE Transactions on Pattern Analysis and Machine Intelligence
Level Set Evolution without Re-Initialization: A New Variational Formulation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
SIOX: Simple Interactive Object Extraction in Still Images
ISM '05 Proceedings of the Seventh IEEE International Symposium on Multimedia
Salient region detection and segmentation
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Unsupervised saliency detection based on 2D Gabor and Curvelets transforms
Proceedings of the Third International Conference on Internet Multimedia Computing and Service
A biologically inspired computational model for image saliency detection
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Bio-inspired visual saliency detection and its application on image retargeting
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Saliency detection based on integrated features
Neurocomputing
Hi-index | 0.00 |
Automatic interesting object extraction is widely used in many image applications. Among various extraction approaches, saliency-based ones usually have a better performance since they well accord with human visual perception. However, nearly all existing saliency-based approaches suffer the integrity problem, namely, the extracted result is either a small part of the object (referred to as sketch-like) or a large region that contains some redundant part of the background (referred to as envelope-like). In this paper, we propose a novel object extraction approach by integrating two kinds of "complementary" saliency maps (i.e., sketch-like and envelope-like maps). In our approach, the extraction process is decomposed into two sub-processes, one used to extract a high-precision result based on the sketch-like map, and the other used to extract a high-recall result based on the envelope-like map. Then a classification step is used to extract an exact object based on the two results. By transferring the complex extraction task to an easier classification problem, our approach can effectively break down the integrity problem. Experimental results show that the proposed approach outperforms six state-of-art saliency-based methods remarkably in automatic object extraction, and is even comparable to some interactive approaches.