@inproceedings{c25edfab95274a2e9c788a9e205081ae,
title = "Spatial constraint for image location estimation",
abstract = "Nowadays, image location has been widely used in many application scenarios for large geo-tagged image corpora. As to images which are not geographically tagged, we can estimate their locations with the help of the large geo-tagged image set by content based image retrieval. In this paper, we propose a global feature clustering and local feature refinement based image location estimation approach. We exploit spatial information by processing useful visual words. In this process, visual word groups are generated. Moreover to improve the retrieval performance, spatial constraint is utilized to code the relative position of visual words. Here we generate a position descriptor for each visual word. Experiments show the effectiveness of our proposed approach.",
keywords = "Bag-of-words, Location estimation, Position descriptor, Visual word group",
author = "Yisi Zhao and Xueming Qian",
year = "2015",
month = jun,
day = "22",
doi = "10.1145/2671188.2749327",
language = "英语",
series = "ICMR 2015 - Proceedings of the 2015 ACM International Conference on Multimedia Retrieval",
publisher = "Association for Computing Machinery",
pages = "515--518",
booktitle = "ICMR 2015 - Proceedings of the 2015 ACM International Conference on Multimedia Retrieval",
note = "5th ACM International Conference on Multimedia Retrieval, ICMR 2015 ; Conference date: 23-06-2015 Through 26-06-2015",
}