IEEE Trans Image Process - Learning-based hierarchical graph for unsupervised matting and foreground estimation.

Tópicos

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Resumo

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.

Resumo Limpo

automat extract foreground object natur imag remain challeng task paper present learningbas hierarch graph unsupervis mat propos hierarch framework progress condens imag data pixel cell cell compon final compon mat layer first propos framework graphbas contract process propos condens imag pixel cell order reduc comput load subsequ process cell map mat compon use spectral cluster learn base graph graph affin effici learnt imag patch differ resolut inclus multiscal inform can effect improv perform spectral cluster final stage hierarch scheme propos multilay foreground estim process assembl mat compon set mat layer unlik convent approach typic address binari foregroundbackground partit propos method provid set multilay interpret unsupervis mat experiment result show propos approach can generat consist accur result compar stateoftheart techniqu

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