Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
The Journal of Machine Learning Research
Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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ACM SIGGRAPH 2004 Papers
Semantic image classification with hierarchical feature subset selection
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
AnnoSearch: Image Auto-Annotation by Search
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
VideoSense: towards effective online video advertising
Proceedings of the 15th international conference on Multimedia
Improving relevance judgment of web search results with image excerpts
Proceedings of the 17th international conference on World Wide Web
Introduction to Information Retrieval
Introduction to Information Retrieval
Argo: intelligent advertising by mining a user's interest from his photo collections
Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising
Argo: intelligent advertising made possible from users' photos
MM '09 Proceedings of the 17th ACM international conference on Multimedia
GameSense: game-like in-image advertising
Multimedia Tools and Applications
ImageSense: Towards contextual image advertising
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Exploiting content relevance and social relevance for personalized ad recommendation on internet TV
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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We present in this paper a new channel to deliver online advertisements along with Web images and show a new business model to monetize billions of Web images. The idea is intuitively inspired by image displaying processes on the Web, which typically require people to wait a few seconds before they see full resolution images. This is due to large file sizes and limited network bandwidth. To utilize idle time and the display area, we propose an innovative method for non-intrusively embedding ads into images in a visually pleasant manner. To maintain a smooth user experience, we utilize the thumbnail of the full-resolution image because it is small and visually similar to the full-resolution image. At the client side, a rendering engine first enlarges and blurs the thumbnail, and then blends the pre-chosen ads information into the enlarged image. Based on this idea, we propose three typical scenarios that can adopt the proposed image-advertising mode. More importantly, we can encourage providers of images or other users to participate in our online image ads service by tagging or annotating images. We envision revenue sharing with the providers participating in our service, and we expect that a large number of users will actively submit, tag and annotate images using the system. We have implemented a prototype image ads system, and conducted a series of experiments and user studies to evaluate such a new advertisement channel. The experimental results and user studies show that the proposed online image ad delivery is a non-intrusive ads mode, and the proposed solution is practical. This work also opens multiple new research directions ranging from multimedia to web data mining