TY - GEN
T1 - Image taken place estimation via geometric constrained spatial layer matching
AU - Zhao, Yisi
AU - Qian, Xueming
AU - Mu, Tingting
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - In recent years, estimating the locations of images has received a lot of attention, which plays a role in application scenarios for large geo-tagged image corpora. So, as to images which are not geographically tagged, we could estimate their locations with the help of the large geo-tagged image set by visual mining based approach. In this paper, we propose a global feature clustering and local feature refinement based image location estimation approach. Firstly, global feature clustering is utilized. We further treat each cluster as a single observation. Next we mine the relationship of each image cluster and locations offline. By cluster selection online, several refined locations likely to be related to an input image are pre-selected. Secondly, we localize the input image by local feature matching which utilizes the “SIFT” descriptor extracted from the refined images. In this process, “spatial layers of visual word” (SLW) is built as an extension of the unorganized bag-of-words image representation. Experiments show the effectiveness of our proposed approach.
AB - In recent years, estimating the locations of images has received a lot of attention, which plays a role in application scenarios for large geo-tagged image corpora. So, as to images which are not geographically tagged, we could estimate their locations with the help of the large geo-tagged image set by visual mining based approach. In this paper, we propose a global feature clustering and local feature refinement based image location estimation approach. Firstly, global feature clustering is utilized. We further treat each cluster as a single observation. Next we mine the relationship of each image cluster and locations offline. By cluster selection online, several refined locations likely to be related to an input image are pre-selected. Secondly, we localize the input image by local feature matching which utilizes the “SIFT” descriptor extracted from the refined images. In this process, “spatial layers of visual word” (SLW) is built as an extension of the unorganized bag-of-words image representation. Experiments show the effectiveness of our proposed approach.
KW - Bag-of-words
KW - Location estimation
KW - Spatial layer matching
UR - https://www.scopus.com/pages/publications/84927796787
U2 - 10.1007/978-3-319-14442-9_49
DO - 10.1007/978-3-319-14442-9_49
M3 - 会议稿件
AN - SCOPUS:84927796787
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 436
EP - 446
BT - MultiMedia Modeling - 21st International Conference, MMM 2015, Proceedings
A2 - He, Xiangjian
A2 - Tao, Dacheng
A2 - Hasan, Muhammad Abul
A2 - Luo, Suhuai
A2 - Xu, Changsheng
A2 - Yang, Jie
PB - Springer Verlag
T2 - 21st International Conference on MultiMedia Modeling, MMM 2015
Y2 - 5 January 2015 through 7 January 2015
ER -