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首页> 外文期刊>Journal of visual communication & image representation >Interactive image segmentation based on samples reconstruction and FLDA
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Interactive image segmentation based on samples reconstruction and FLDA

机译：基于样本重构和FLDA的交互式图像分割

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摘要

Existing interactive image segmentation methods heavily rely on manual input, i.e. a sufficient quantity and correct locations of labels. In this paper, we propose a new interactive segmentation algorithm which aims to reduce human intervention and to generate high-quality segmentation results. In contrast to most energy minimizing based segmentation methods, the segmentation is cast as multi-classification in our proposed method. First, the input image is segmented into superpixels by using different methods. Then we build a dictionary consisting of all obtained superpixels and reconstruct samples represented by certain labeled superpixels. Finally, we learn a discriminative projection matrix through Fishers linear discriminant analysis (FLDA) algorithm, which learns a discriminative subspace for classification. The unlabeled superpixels are grouped into foreground or background, via calculating their minimal norm. Our method can capture long range grouping cues and reduce the sensitivity with respect to input label quantity and location of labels, by the combination of superpixels and discriminative dictionary. Extensive experiments are conducted both on MSRC and another challenging database in order to demonstrate the effectiveness of the proposed method. Quantitative and qualitative results show that our method is competitive to the state-of-the-art performance. (C) 2016 Elsevier Inc. All rights reserved.
机译：现有的交互式图像分割方法在很大程度上依赖于手动输入，即足够数量和正确的标签位置。在本文中，我们提出了一种新的交互式分割算法，旨在减少人为干预并生成高质量的分割结果。与大多数基于能量最小化的分割方法相比，在我们提出的方法中，分割被转换为多分类。首先，通过使用不同的方法将输入图像分割成超像素。然后，我们构建一个包含所有获得的超像素的字典，并重建由某些标记的超像素表示的样本。最后，我们通过Fishers线性判别分析（FLDA）算法学习判别投影矩阵，该算法学习判别子空间进行分类。通过计算未标记的超像素的最小范数，可以将其分类为前景或背景。通过结合超像素和判别词典，我们的方法可以捕获远距离分组提示并降低相对于输入标签数量和标签位置的敏感性。为了证明所提方法的有效性，在MSRC和另一个具有挑战性的数据库上进行了广泛的实验。定量和定性结果表明，我们的方法与最先进的性能相比具有竞争力。 （C）2016 Elsevier Inc.保留所有权利。

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