標題: Learning-Based Hierarchical Graph for Unsupervised Matting and Foreground Estimation
作者: Tseng, Chen-Yu
Wang, Sheng-Jyh
電子工程學系及電子研究所
Department of Electronics Engineering and Institute of Electronics
關鍵字: Image matting;spectral graph;segmentation
公開日期: 1-十二月-2014
摘要: Automatically extracting foreground objects from a natural image remains a challenging task. This paper presents a learning-based hierarchical graph for unsupervised matting. The proposed hierarchical framework progressively condenses image data from pixels into cells, from cells into components, and finally from components into matting layers. First, in the proposed framework, a graph-based contraction process is proposed to condense image pixels into cells in order to reduce the computational loads in the subsequent processes. Cells are further mapped into matting components using spectral clustering over a learning based graph. The graph affinity is efficiently learnt from image patches of different resolutions and the inclusion of multiscale information can effectively improve the performance of spectral clustering. In the final stage of the hierarchical scheme, we propose a multilayer foreground estimation process to assemble matting components into a set of matting layers. Unlike conventional approaches, which typically address binary foreground/background partitioning, the proposed method provides a set of multilayer interpretations for unsupervised matting. Experimental results show that the proposed approach can generate more consistent and accurate results as compared with state-of-the-art techniques.
URI: http://dx.doi.org/10.1109/TIP.2014.2323132
http://hdl.handle.net/11536/123888
ISSN: 1057-7149
DOI: 10.1109/TIP.2014.2323132
期刊: IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume: 23
Issue: 12
顯示於類別:期刊論文


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  1. 000344156300001.pdf