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.
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.
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