Nonlinear Regression of Saliency Guided Proposals for Unsupervised Segmentation of Dynamic Scenes

作者：Zhang, Yinhui^*; Abdel-Mottaleb, Mohamed^*; He, Zifen^*

来源：IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2016, E99D(2): 467-474.

DOI：10.1587/transinf.2015EDP7295

• video object segmentation
• salient object-like proposal
• nonlinear regressor
• dynamic scene
• random forest

This paper proposes an efficient video object segmentation approach that is tolerant to complex scene dynamics. Unlike existing approaches that rely on estimating object-like proposals on an intra-frame basis, the proposed approach employs temporally consistent foreground hypothesis using nonlinear regression of saliency guided proposals across a video sequence. For this purpose, we first generate salient foreground proposals at superpixel level by leveraging a saliency signature in the discrete cosine transform domain. We propose to use a random forest based nonlinear regression scheme to learn both appearance and shape features from salient foreground regions in all frames of a sequence. Availability of such features can help rank every foreground proposals of a sequence, and we show that the regions with high ranking scores are well correlated with semantic foreground objects in dynamic scenes. Subsequently, we utilize a Markov Random Field to integrate both appearance and motion coherence of the top-ranked object proposals. A temporal nonlinear regressor for generating salient object support regions significantly improves the segmentation performance compared to using only per-frame objectness cues. Extensive experiments on challenging real-world video sequences are performed to validate the feasibility and superiority of the proposed approach for addressing dynamic scene segmentation.

• 出版日期2016-2
• 单位昆明理工大学