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Salient Object Detection Method Based on Multiple Semantic Features

机译：基于多重语义特征的显着目标检测方法

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摘要

The existing salient object detection model can only detect the approximate location of salient object, or highlight the background, to resolve the above problem, a salient object detection method was proposed based on image semantic features. First of all, three novel salient features were presented in this paper, including object edge density feature (EF). object semantic feature based on the convex hull (CF) and object lightness contrast feature (LF). Secondly, the multiple salient features were trained with random detection windows. Thirdly, Naive Bayesian model was used for combine these features for salient detection. The results on public datasets showed that our method performed well, the location of salient object can be fixed and the salient object can be accurately detected and marked by the specific window.
机译：为了解决上述问题，现有的显着物体检测模型只能检测到显着物体的大致位置，或者只能突出显示背景，以解决上述问题，提出了一种基于图像语义特征的显着物体检测方法。首先，本文提出了三种新颖的显着特征，包括物体边缘密度特征（EF）。基于凸包（CF）和物体亮度对比特征（LF）的物体语义特征。其次，使用随机检测窗口对多个显着特征进行训练。第三，朴素贝叶斯模型用于结合这些特征进行显着检测。在公共数据集上的结果表明，我们的方法效果良好，可以固定显着物体的位置，并且可以通过特定窗口准确地检测和标记显着物体。

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