Co-saliency detection via integration of multi-layer convolutional features and inter-image propagation

Convolutional neural networks have been successfully applied to detect salient objects in an image. However, how to better use convolutional features for co-saliency detection, which is an emerging branch of saliency detection, is not fully explored. This paper proposes a convolutional neural network based co-saliency detection model, which consists of two key parts including the integration of multi-layer convolutional features extracted from a group of images and the inter-image saliency propagation. Firstly, the input image and its four co-images belonging to the same image category are passed through the VGG16 model, to obtain the multi-layer convolutional features of these images. Secondly, multi-scale synthesized feature maps, which contain both internal features and correlative features, are generated by integrating the multi-layer convolutional features. Thirdly, via the integration of low-level boundary features and high-level semantic features, the multi-scale synthesized feature maps are enhanced and fused together to generate the initial co-saliency map. Finally, an inter-image saliency propagation method is utilized to refine the initial co-saliency map, yielding the final co-saliency map with the improved quality. Experimental results on two public datasets demonstrate that the proposed model achieves the best performance compared to the state-of-the-art co-saliency detection models.