Background Prior-Based Salient Object Detection via Deep Reconstruction Residual
Background Prior-Based Salient Object Detection via Deep Reconstruction Residual
复制标题
通过深度重构残差进行基于背景先验的显着目标检测
DOI：
10.1109/tcsvt.2014.2381471
复制
发表时间：
2015-08-01
影响因子：
8.4
通讯作者：
Wu, Feng
中科院分区：
文献类型：
--
作者：
Han, Junwei;Zhang, Dingwen;Wu, Feng
近年来，从图像中检测显著目标获得了越来越多的研究兴趣，因为它可以大大促进广泛的基于内容的多媒体应用。基于前景显著区域在特定背景下是独特的假设，大多数传统方法依赖于许多手工设计的特征，并且使用局部或全局对比度来测量它们的独特性。虽然这些方法已被证明是有效的，在处理简单的图像，其有限的能力可能会导致困难时，处理更复杂的图像。提出了一种新的显著性检测框架，该框架首先对背景进行建模，然后从背景中分离出显著性对象。我们开发了具有深度学习架构的堆栈去噪自动编码器，以模拟探索潜在模式的背景，并以无监督和自下而上的方式学习更强大的数据表示。之后，我们将显著对象与背景的分离公式化为测量深度自编码器的重建残差的问题。三个基准数据集的综合评价和九个国家的最先进的算法的比较证明了本文的优越性。
Detection of salient objects from images is gaining increasing research interest in recent years as it can substantially facilitate a wide range of content-based multimedia applications. Based on the assumption that foreground salient regions are distinctive within a certain context, most conventional approaches rely on a number of hand-designed features and their distinctiveness is measured using local or global contrast. Although these approaches have been shown to be effective in dealing with simple images, their limited capability may cause difficulties when dealing with more complicated images. This paper proposes a novel framework for saliency detection by first modeling the background and then separating salient objects from the background. We develop stacked denoising autoencoders with deep learning architectures to model the background where latent patterns are explored and more powerful representations of data are learned in an unsupervised and bottom-up manner. Afterward, we formulate the separation of salient objects from the background as a problem of measuring reconstruction residuals of deep autoencoders. Comprehensive evaluations of three benchmark datasets and comparisons with nine state-of-the-art algorithms demonstrate the superiority of this paper.