Integrated Bidirectional Attention Salient Region Detection Based on Full Convolution and Encoder-Decoder

To locate salient regions accurately in complex backgrounds and optimize sparseness of the salient re- gions, we propose a salient region detection model based on fully convolutional networks and encoder-decoder, which consider both bottom-up and top-down attention information. Firstly, we build a fully convolutional net- work which incorporates a symmetric decoding operation. Then, in the process of decoding, we further concate- nate the high-level features with the low-level high-resolution features. Finally, we use the least-square estimator method to estimate the optimal weights and obtain the final saliency map. We conduct extensive experiments on five public datasets. The experimental results demonstrate that our model is effective and accurate to salient re- gion detection compared with state-of-art methods.