@inproceedings{119665fed66b4796b427e3734ca01064,
title = "Co-saliency detection based on siamese network",
abstract = "Saliency detection in images attracts much research attention for its usage in numerous multimedia applications. Beside on the detection within the single image, co-saliency has been developed rapidly by detecting the same foreground objects in different images and trying to further promote the performance of object detection. This paper we propose a co-saliency detection method based on Siamese Network. By using Siamese Network, we get the similarity matrix of each image in superpixels. Guided by the single image saliency map, each saliency value, saliency score matrix is obtained to generate the multi image saliency map. Our final saliency map is a linear combination of these two saliency maps. The experiments show that our method performs better than other state-of-arts methods.",
keywords = "Co-saliency detection, Feature extraction, Siamese network",
author = "Zhengchao Lei and Weiyan Chai and Sanyuan Zhao and Hongmei Song and Fengxia Li",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2018.; 13th International Conference on Mobile Ad-hoc and Sensor Networks, MSN 2017 ; Conference date: 17-12-2017 Through 20-12-2017",
year = "2018",
doi = "10.1007/978-981-10-8890-2\_8",
language = "English",
isbn = "9789811088896",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "99--109",
editor = "Liehuang Zhu and Sheng Zhong",
booktitle = "Mobile Ad-hoc and Sensor Networks - 13th International Conference, MSN 2017, Revised Selected Papers",
address = "Germany",
}