Top–Down Saliency Detection Based on Deep-Learned Features
Duzhen Zhang, Zakir Ali · International Journal of Computational Intelligence and Applications · 2019
How to localize objects in images accurately and efficiently is a challenging problem in computer vision. In this paper, a novel top–down fine-grained salient object detection method based on deep-learned features is proposed, which can detect the same object in input image as the query image. The query image and its three subsample images are used as top–down cues to guide saliency detection. We ameliorate convolutional neural network (CNN) using the fast VGG network (VGG-f) pre-trained on ImageNet and re-trained on the Pascal VOC 2012 dataset. Experiment on the FiFA dataset demonstrates that proposed method can localize the saliency region and find the specific object (e.g., human face) as the query. Experiments on the David1 and Face1 sequences conclusively prove that the proposed algorithm is able to effectively deal with many challenging factors including illumination change, shape deformation, scale change and partial occlusion.