Inspired by the perceived saturation of human visual system, this paper proposes a two-stream hybrid networks to simulate binocular vision for salient object detection (SOD). Each stream in our system consists of unsupervised and supervised methods to form a two-branch module, so as to model the interaction between human intuition and memory. The two-branch module parallel processes visual information with bottom-up and top-down SODs, and output two initial saliency maps. Then a polyharmonic neural network with random-weight (PNNRW) is utilized to fuse two-branch’s perception and refine the salient objects by learning online via multi-source cues. Depend on visual perceptual saturation, we can select optimal parameter of superpixel for unsupervised branch, locate sampling regions for PNNRW, and construct a positive feedback loop to facilitate perception saturated after the perception fusion. By comparing the binary outputs of the two-stream, the pixel annotation of predicted object with high saturation degree could be taken as new training samples. The presented method constitutes a semi-supervised learning framework actually. Supervised branches only need to be pre-trained initial, the system can collect the training samples with high confidence level and then train new models by itself. Extensive experiments show that the new framework can improve performance of the existing SOD methods, that exceeds the state-of-the-art methods in six popular benchmarks.

中文翻译：

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基于视觉感知饱和度和两流混合网络的显着目标检测

受人类视觉系统感知饱和的启发，本文提出了一种两流混合网络来模拟双目视觉以进行显着物体检测（SOD）。我们系统中的每个流都由无监督和受监督的方法组成，以形成一个两分支模块，从而对人类直觉和记忆之间的交互进行建模。两分支模块通过自下而上和自上而下的SOD并行处理视觉信息，并输出两个初始显着性图。然后利用具有随机权重的多谐波神经网络（PNNRW）融合两分支的感知并通过多源线索在线学习来精炼显着对象。根据视觉感知的饱和度，我们可以为无监督分支选择超像素的最佳参数，为PNNRW定位采样区域，并构建正反馈回路以促进感知融合后的感知饱和。通过比较两个流的二进制输出，可以将饱和度高的预测对象的像素注释作为新的训练样本。所提出的方法实际上构成了一个半监督的学习框架。受监管的分支机构只需要进行初始训练，系统就可以以高置信度收集训练样本，然后自行训练新模型。大量的实验表明，新框架可以提高现有SOD方法的性能，超过了六个流行基准中的最新方法。具有较高饱和度的预测对象的像素标注可以作为新的训练样本。所提出的方法实际上构成了一个半监督的学习框架。受监管的分支机构只需要进行初始训练，系统就可以以高置信度收集训练样本，然后自行训练新模型。大量的实验表明，新框架可以提高现有SOD方法的性能，超过了六个流行基准中的最新方法。具有较高饱和度的预测对象的像素标注可以作为新的训练样本。所提出的方法实际上构成了一个半监督的学习框架。受监管的分支只需要预先进行训练，系统可以以高置信度收集训练样本，然后自行训练新模型。大量的实验表明，新框架可以提高现有SOD方法的性能，超过了六个流行基准中的最新方法。