R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Efficient Cost Models for Spatial Queries Using R-Trees
IEEE Transactions on Knowledge and Data Engineering
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Automatic selection of representative photo and smart thumbnailing using near-duplicate detection
MM '08 Proceedings of the 16th ACM international conference on Multimedia
An online advertisement platform based on image content bidding
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Optimization of geographic area to a web page for two-dimensional range query processing
WISEW'03 Proceedings of the Fourth international conference on Web information systems engineering workshops
Proceedings of the international conference on Multimedia
Personalized travel recommendation by mining people attributes from community-contributed photos
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Location Discriminative Vocabulary Coding for Mobile Landmark Search
International Journal of Computer Vision
Mobile-based advertisement information retrieval from images and websites
Proceedings of the 20th ACM international conference on Multimedia
Learning from mobile contexts to minimize the mobile location search latency
Image Communication
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This paper introduces the architecture of our location sensitive indexing model which is used in a platform designed to deliver advertisements to users who primarily utilize images as queries instead of textual keywords. The indexing model facilitates an advertiser's ability to bid on images, such as billboards or logos, in order to obtain user feedback in judging image attractiveness. Additionally, the model enables automatic evaluation of advertisement popularity by mining users' query logs, which is critical for generating advertisement recommendations. The location sensitive architecture of this model enables effective and efficient functionality in large-scale scenarios. In the model's structure, our Location Sensitive Visual Indexing model (LSVI) incorporates location information that subdivides geographical regions for precise and localized image matching. By collecting feedback from mobile users, location-based mining can also help discover popular advertisements as well as their representative images. We have deployed our platform into a real-world advertising system in Beijing, China, which demonstrates effective results in comparative studies with both alternative and state-of-the-art approaches.