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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Dual Low-Rank Pursuit: Learning Salient Features for Saliency Detection
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Dual Low-Rank Pursuit: Learning Salient Features for Saliency Detection

机译：双重低排名追求：学习显着性检测的显着特征

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摘要

Saliency detection is an important procedure for machines to understand visual world as humans do. In this paper, we consider a specific saliency detection problem of predicting human eye fixations when they freely view natural images, and propose a novel dual low-rank pursuit (DLRP) method. DLRP learns saliency-aware feature transformations by utilizing available supervision information and constructs discriminative bases for effectively detecting human fixation points under the popular low-rank and sparsity-pursuit framework. Benefiting from the embedded high-level information in the supervised learning process, DLRP is able to predict fixations accurately without performing the expensive object segmentation as in the previous works. Comprehensive experiments clearly show the superiority of the proposed DLRP method over the established state-of-the-art methods. We also empirically demonstrate that DLRP provides stronger generalization performance across different data sets and inherits the advantages of both the bottom-up- and top-down-based saliency detection methods.
机译：显着性检测是机器像人类一样理解视觉世界的重要过程。在本文中，我们考虑了预测人眼注视自由观看自然图像时的特定显着性检测问题，并提出了一种新颖的双重低秩追踪（DLRP）方法。 DLRP通过利用可用的监管信息来学习显着性特征转换，并构建区分性基础，以在流​​行的低等级和稀疏性追求框架下有效地检测人体固定点。得益于监督学习过程中嵌入的高级信息，DLRP能够准确预测注视，而无需像以前的工作那样执行昂贵的对象分割。全面的实验清楚地表明，所提出的DLRP方法优于已建立的最新方法。我们还凭经验证明DLRP在不同数据集之间提供了更强的泛化性能，并继承了自下而上和自上而下的显着性检测方法的优点。

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