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首页> 外文期刊>Knowledge and information systems >A novel approach for salient object detection using double-density dual-tree complex wavelet transform in conjunction with superpixel segmentation
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A novel approach for salient object detection using double-density dual-tree complex wavelet transform in conjunction with superpixel segmentation

机译：使用双密度双树复杂小波变换与Superpixel分割结合的一种新颖方法

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摘要

Salient object detection in wavelet domain has recently begun to attract researchers' effort due to its desired ability to provide multi-scale analysis of an image simultaneously in both frequency and spatial domains. The proposed algorithm exploits the inherent multi-scale structure of the double-density dual-tree complex-oriented wavelet transform (DDDTCWT) to decompose each input image into four approximate sub-band images and 32 high-pass detailed sub-band images at each scale. These 32 detailed high-pass sub-bands at each scale are adequate to represent singularities of any geometric object with high precision and to mimic zooming-in and zooming-out process of human vision system. In the proposed model, we first compute a rough segmented saliency map (RSSM) by fusing multi-scale edge-to-texture features generated from DDDTCWT with segmentation results obtained from bipartite graph partitioning-based segmentation approach. Then, each pixel in RSSM is categorized into either background region or salient region based on a threshold. Finally, the pixels of the two regions are considered as samples to be drawn from a multivariate kernel function whose parameters are estimated using expectation maximization algorithm, to generate a saliency map. The performance of the proposed model is evaluated in terms of precision, recall, F-measure, area under the ROC curve and computation time using six publicly available image datasets. Extensive experimental results on six benchmark datasets demonstrate that the proposed model outperformed the existing 29 state-of-the-art methods in terms of F-measure on all five datasets, recall on four datasets and area under ROC curve on two datasets. In terms of mean recall value, mean F-measure value and mean AUC value on all six datasets, the proposed method outperforms all state-of-the-art methods. The proposed method also takes comparatively less computation time in comparison with many existing spatial domain methods.
机译：小波域中的突出对象检测最近开始吸引研究人员的努力，因为它在频率和空间域同时提供了同时图像的多尺度分析。所提出的算法利用双浓度双树复合的小波变换（DDDTCWT）的固有多尺度结构来将每个输入图像分解为四个近似子带图像和32在每个近似的子带图像中和32个高通过详细的子带图像规模。这些32每个规模的详细的高通子带足以表示任何具有高精度的几何对象的奇点，并模拟人类视觉系统的变焦和变焦过程。在所提出的模型中，我们首先通过融合从DDDTCWT生成的多尺度边缘到纹理特征来计算粗糙分段的显着性图（RSSM），并从基于二角形图分区的分割方法获得的分段结果。然后，RSSM中的每个像素基于阈值分类为背景区域或突出区域。最后，认为两个区域的像素被认为是从多变量内核函数汲取的样本，其参数使用期望的最大化算法估计，以产生显着图。在ROC曲线下的精度，召回，F度量，面积和使用六个公开的图像数据集的计算时间来评估所提出的模型的性能。在六个基准数据集上的广泛实验结果表明，所提出的模型在所有五个数据集的F测量方面表现出现有的29个最先进的方法，在两个数据集中的ROC曲线下的四个数据集和面积上召回。就平均召回值而言，在所有六个数据集上的平均值值和平均AUC值，所提出的方法优于所有最先进的方法。与许多现有的空间域方法相比，所提出的方法也相对较少的计算时间。

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