关键词: 显著性检测, 深度交叉卷积神经网络（CNN）, 超像素；Beltrami滤波, 条件随机场（CRF）, 免交互GrabCut

Abstract: Aiming at the problem that traditional saliency detection methods suffer from insufficient feature learning, unclear boundary and bad robust detection, a saliency detection algorithm based on deep cross CNN and non-interaction GrabCut is proposed. First, focusing on the hard training caused by the huge amount of neurons and parameters, a deep cross CNN model is proposed combined with the principle of human vision. Thereafter, images regions features are obtained by using superpixels and the edges are extracted by the Beltrami filtering. The features are identified by the DCCNN model and fused under CRF to gain a coarse saliency region. Finally, it binaries and dilates the rough detection results adaptively, inputs the polygonal approximation into GrabCut algorithm to achieve precise saliency detection. Experimental results show that the proposed algorithm improves the detection precision greatly, which is more robust and universal.

Key words: aliency detection, deep cross Convolutional Neural Network(CNN), superpixel, Beltrami filter, Conditional Random Field(CRF), non-interaction GrabCut