Image Taken Place Estimation via Geometric Constrained Spatial Layer Matching

Zhao, Yisi; Qian, Xueming; Mu, Tingting

doi:10.1007/978-3-319-14442-9_49

Cited by 3 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our image location estimation is purely based on image content. We can convert the image location estimation problem as content based image retrieval [25], [55]- [57], [64] or object recognition problem [58]- [61]. In social media community, the shared photos are always attached with tags, time stamps, user's comments, and geo-coordinates that images are taken.…”

Section: Related Workmentioning

confidence: 99%

Image Location Estimation by Salient Region Matching

Qian

Zhao

Han

2015

IEEE Trans. on Image Process.

Self Cite

View full text Add to dashboard Cite

Nowadays, locations of images have been widely used in many application scenarios for large geo-tagged image corpora. As to images which are not geographically tagged, we estimate their locations with the help of the large geo-tagged image set by content-based image retrieval. In this paper, we exploit spatial information of useful visual words to improve image location estimation (or content-based image retrieval performances). We proposed to generate visual word groups by mean-shift clustering. To improve the retrieval performance, spatial constraint is utilized to code the relative position of visual words. We proposed to generate a position descriptor for each visual word and build fast indexing structure for visual word groups. Experiments show the effectiveness of our proposed approach.

show abstract