BibTex format
@inproceedings{Li:2019,
author = {Li, M and Dong, S and Zhang, K and Gao, Z and Wu, X and Zhang, H and Yang, G and Li, S},
title = {Deep Learning intra-image and inter-images features for Co-saliency detection},
year = {2019}
}
Citation
RIS format (EndNote, RefMan)
TY - CPAPER
AB - In this paper, we propose a novel deep end-to-end co-saliency detection approach to extract common salient objects from images group. The existing approaches rely heavily on manually designed metrics to characterize co-saliency. However, these methods are so subjective and not flexible enough that leads to poor generalization ability. Furthermore, most approaches separate the process of single image features and group images features extraction, which ignore the correlation between these two features that can promote the model performance. The proposed approach solves these two problems by multistage representation to extract features based on high-spatial resolution CNN. In addition, we utilize the modified CAE to explore the learnable consistency. Finally, the intra-image contrast and the inter-images consistency are fused to generate the final co-saliency maps automatically among group images by multistage learning. Experiment results demonstrate the effectiveness and superiority of our approach beyond the state-of-the-art methods.
AU - Li,M
AU - Dong,S
AU - Zhang,K
AU - Gao,Z
AU - Wu,X
AU - Zhang,H
AU - Yang,G
AU - Li,S
PY - 2019///
TI - Deep Learning intra-image and inter-images features for Co-saliency detection
ER -
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