Optimal contrast based saliency detection
Optimal contrast based saliency detection
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DOI：
10.1016/j.patrec.2013.04.009
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发表时间：
2013-08
期刊：
影响因子：
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通讯作者：
Xiaoliang Qian;Junwei Han;Gong Cheng;Lei Guo
中科院分区：
文献类型：
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作者：
Xiaoliang Qian;Junwei Han;Gong Cheng;Lei Guo
近年来，显着性检测受到越来越多的关注，因为它可以显着促进许多基于内容的多媒体应用。大多数传统方法采用预定义的局部对比度、全局对比度或它们的启发式组合来测量显着性。在本文中，基于人类视觉注意机制自适应地适应各种尺度并且显着物体可以相对于特定周围区域内的背景最大程度地突出的基本前提，我们提出了一种使用最佳对比度的新概念的新颖的显着性检测方法。首先通过稀疏编码原理与各种周围区域计算多个对比假设。然后，使用基于熵的标准对这些假设进行比较，并选择最佳对比度，将其作为构建显着图的核心因素。最后，进行多尺度增强以进一步细化结果。对三个公开可用的基准数据集的综合评估以及与许多最新算法的比较证明了所提出的工作的有效性。
Saliency detection has been gaining increasing attention in recent years since it could significantly boost many content-based multimedia applications. Most traditional approaches adopt the predefined local contrast, global contrast, or heuristic combination of them to measure saliency. In this paper, based on the underlying premises that human visual attention mechanisms work adaptively for various scales and salient objects can maximally pop out with respect to the background within a specific surrounding area, we propose a novel saliency detection method using a new concept of optimal contrast. A number of contrast hypotheses are first calculated with various surrounding areas by means of sparse coding principles. Afterwards, these hypotheses are compared using an entropy-based criterion and the optimal contrast is selected which is treated as the core factor for building the saliency map. Finally, a multi-scale enhancement is performed to further refine the results. Comprehensive evaluations on three publicly available benchmark datasets and comparisons with many up-to-date algorithms demonstrate the effectiveness of the proposed work.