Saliency detection method based on graph node centrality and spatial autocorrelation

doi:10.11772/j.issn.1001-9081.2018050983

Abstract

Abstract: The salient area detected by the existing saliency detection methods has the problems of uneven endoplasm, not clear and accurate boundary. In order to solve the problems, a saliency detection method based on spatial autocorrelation and importance evaluation strategy of graph nodes in complex networks was proposed. Firstly, combined with color information and spatial information, a saliency initial graph under multi-criteria was generated by using the centrality rules of complex network nodes and the spatial autocorrelation indicator coefficient. Then, Dempster-Shafer (D-S) evidence theory was used to fuse multiple initial graphs, and the final salient region results were obtained by adding boundary strength information to a progressively optimized two-stage cellular automaton. The single-step validity verification was performed for each module in the main process of the proposed method on two public image data sets, and the experimental comparisons in visual qualitative index, objective quantitative index and algorithmic efficiency were performed between the proposed method and the other existing saliency detection methods. The experimental results show that, the proposed method is effective in single-step modules, and is superior to other algorithms in terms of the comprehensive results of significant visual effects, Precision-Recall (P-R) curve, F-measure value, Mean Absolute Error (MAE) and algorithm time-consuming, especially to the Background-based maps optimized by Single-layer Cellular Automata (BSCA) algorithm closely related to the proposed method. At the same time, the results of visual contrast experiments also verify that, the proposed method can effectively improve the unsatisfactory results of uneven endoplasm, unclear boundary due to the small difference between salient objects and image background, and the large difference in the internal color of salient objects.

Key words: saliency detection, node centrality, spatial autocorrelation, Dempster-Shafer (D-S) evidence theory, cellular automata

摘要： 针对现有显著性检测方法检测出的显著性区域内质不均匀、边界不够清晰准确的问题，提出了一种基于复杂网络图节点重要性评估策略以及空间自相关的显著性检测方法。首先，结合颜色信息、空间信息，利用复杂网络节点中心性法则以及空间自相关指示系数，生成多准则下的显著性初始图；然后，利用D-S证据理论融合多幅初始图，通过将边界强度信息加入到递进优化的二级元胞自动机，从而得到最终显著性区域结果。在两个公开图像数据集上对所提方法主体过程各模块进行了单步有效性验证，并与其他现有显著性检测方法进行视觉定性、客观定量以及算法效率的实验对比。实验结果表明，所提方法单步各模块具有有效性，且在显著性视觉效果，以及准确率-召回率（P-R）曲线、F-measure值和平均绝对误差（MAE）、算法耗时的综合结果上优于其他算法，特别是与所提方法密切相关的BSCA算法。同时，视觉对比实验的结果也验证了所提方法能够有效改善由于显著性物体与图像背景差异小、显著性物体内部颜色不一且差异大而产生的内质不均和边界不明确的不理想结果。

关键词: 显著性检测, 节点中心性, 空间自相关, Dempster-Shafer证据理论, 元胞自动机

CLC Number:

TP391.41

WANG Shasha, FENG Ziliang, FU Keren. Saliency detection method based on graph node centrality and spatial autocorrelation[J]. Journal of Computer Applications, 2018, 38(12): 3547-3556.

王莎莎, 冯子亮, 傅可人. 基于图节点中心性和空间自相关的显著性检测方法[J]. 计算机应用, 2018, 38(12): 3547-3556.

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