Image Location Inference by Multisaliency Enhancement

Locations of images have been widely used in many application scenarios for large geotagged image corpora. As to images that are not geographically tagged, we estimate their locations with the help of the large geotagged image set by content-based image retrieval. Bag-of-words image representation h...

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Bibliographic Details
Published in:IEEE transactions on multimedia 2017-04, Vol.19 (4), p.813-821
Main Authors: Qian, Xueming, Wang, Huan, Zhao, Yisi, Hou, Xingsong, Hong, Richang, Wang, Meng, Tang, Yuan Yan
Format: Article
Language:English
Subjects:
Estimation
Feature extraction
Image retrieval
Location estimation
region of interest (ROI)
salient map
salient region
Solid modeling
spatial constraint
Three-dimensional displays
visual phrase
Visualization
Vocabulary
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Summary:Locations of images have been widely used in many application scenarios for large geotagged image corpora. As to images that are not geographically tagged, we estimate their locations with the help of the large geotagged image set by content-based image retrieval. Bag-of-words image representation has been utilized widely. However, the individual visual word-based image retrieval approach is not effective in expressing the salient relationships of image region. In this paper, we present an image location estimation approach by multisaliency enhancement. We first extract region-of-interests (ROIs) by mean-shift clustering on the visual words and salient map of the image based on which we further determine the importance of the ROI. Then, we describe each ROI by the spatial descriptors of visual words. Finally, region-based visual phrases are generated to further enhance the saliency in image location estimation. Experiments show the effectiveness of our proposed approach.
ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2016.2638207