An Improved Hybrid Model for Automatic Salient Region Detection

In this letter, the graph-based visual saliency (GBVS) model is extended by pulse-coupled neural network (PCNN) to implement the well-defined criteria for a saliency detector. In receptive field, the resized intensity feature map generated by GBVS was regarded as the input image of the PCNN. After modulation, the optimal iteration number and threshold were identified by GBVS and Otsu´s method in pulse generator part, respectively. Moreover, other parameters of the PCNN were set automatically. In the end, an automatic salient region detection algorithm was proposed. Experimental results show that our proposed hybrid model can efficiently detect salient region.