In computer vision, the salient object detection problem consists of finding the most attention-grabbing objects in images. In the last years, many researchers have proposed salient object detection algorithms to address this problem. However, most of the algorithms perform well only on images with specific conditions and they do not solve the general problem. To cope with a more significant number of image types than those where each standalone saliency detection method performs well, novel methods search to generate a combination model that improves the overall performance of detecting salient objects in images. The contribution of this work is oriented towards the automatic design of combination models by using genetic programming. The proposed approach automatically selects the algorithms to be combined and the combination operators that result in an improvement in the overall performance. The evolutionary approach uses as input a set of candidate saliency detection methods and a set of combination operators. The set of input saliency detection methods includes algorithms from the state-of-the-art. The set of combination operators includes fuzzy logic combination rules, morphological operations, and image processing filters. The outcome of each run of the evolutionary process is a combination model that describes how the input models have to be combined. An advantage of the proposed approach is that these models explain and give insight about which standalone methods are important to improve the response in the solution of the saliency detection problem. The improvement of the final combination models is demonstrated by comparing their performance against that of several state-of-the-art saliency detection methods, that of several classic combination models, and that of other evolutionary computation-based approaches, on four benchmark datasets. The results were analyzed using two statistical tests, the Wilcoxon rank-sum test, and the t-test. Both tests confirmed that the proposed approach outperforms all of the other algorithms under test and that its performance advantage is statistically significant.

中文翻译：

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显着目标检测组合模型自动设计中的遗传编程框架

在计算机视觉中，显着对象检测问题包括在图像中找到最引人注目的对象。在过去的几年里，许多研究人员提出了显着目标检测算法来解决这个问题。然而，大多数算法仅在具有特定条件的图像上表现良好，并不能解决一般问题。为了处理比每种独立显着性检测方法表现良好的图像类型更多的图像类型，新方法搜索以生成组合模型，以提高检测图像中显着对象的整体性能。这项工作的贡献是面向使用遗传编程的组合模型的自动设计。所提出的方法自动选择要组合的算法和组合运算符，从而提高整体性能。进化方法使用一组候选显着性检测方法和一组组合算子作为输入。输入显着性检测方法集包括最先进的算法。组合算子集包括模糊逻辑组合规则、形态学操作和图像处理过滤器。进化过程每次运行的结果是一个组合模型，它描述了输入模型必须如何组合。所提出的方法的一个优点是，这些模型解释并提供了有关哪些独立方法对于改善显着性检测问题解决方案中的响应很重要的见解。通过在四个基准数据集上将它们的性能与几种最先进的显着性检测方法、几种经典组合模型和其他基于进化计算的方法的性能进行比较，证明了最终组合模型的改进。使用两种统计检验（Wilcoxon 秩和检验和 t 检验）分析结果。两项测试都证实，所提出的方法优于所有其他被测算法，并且其性能优势在统计上是显着的。使用两种统计检验（Wilcoxon 秩和检验和 t 检验）分析结果。两项测试都证实，所提出的方法优于所有其他被测算法，并且其性能优势在统计上是显着的。使用两种统计检验（Wilcoxon 秩和检验和 t 检验）分析结果。两项测试都证实，所提出的方法优于所有其他被测算法，并且其性能优势在统计上是显着的。