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Conference Paper
Unsupervised Salient Object Matting
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- Authors
- Jaehwan Kim, Jongyoul Park
- Issue Date
- 2015-10
- Citation
- International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS) 2015 (LNCS 9386), pp.752-763
- ISSN
- 0302-9743
- Publisher
- Springer
- Language
- English
- Type
- Conference Paper
- Abstract
- In this paper, we present a new, easy-to-generate method that is capable of precisely matting salient objects in a large-scale image set in an unsupervised way. Our method extracts only salient object without any user-specified constraints or a manual-thresholding of the saliency-map, which are essentially required in the image matting or saliency-map based segmentation, respectively. In order to provide a more balanced visual saliency as a response to both local features and global contrast, we propose a new, coupled saliency-map based on a linearly combined conspicuity map. Also, we introduce an adaptive tri-map as a refined segmented image of the coupled saliency-map for amore precise object extraction. The proposed method improves the segmentation performance, compared to image matting based on two existing saliency detection measures. Numerical experiments and visual comparisons with large-scale real image set confirm the useful behavior of the proposed method.
- KSP Keywords
- Image Matting, Large scale image, Local features, Numerical experiments, Saliency detection, global contrast, image set, object extraction, salient object, visual saliency