Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
WWW '03 Proceedings of the 12th international conference on World Wide Web
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Retrieval evaluation with incomplete information
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Randomized Trees for Real-Time Keypoint Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Categorization in multiple category systems
ICML '06 Proceedings of the 23rd international conference on Machine learning
HT06, tagging paper, taxonomy, Flickr, academic article, to read
Proceedings of the seventeenth conference on Hypertext and hypermedia
Why we tag: motivations for annotation in mobile and online media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
ACM Transactions on the Web (TWEB)
How many high-level concepts will fill the semantic gap in news video retrieval?
Proceedings of the 6th ACM international conference on Image and video retrieval
Diversifying image search with user generated content
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Adding Semantics to Detectors for Video Retrieval
IEEE Transactions on Multimedia
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Knowledge discovery over community-sharing media: from signal to intelligence
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Automatic region of interest detection in tagged images
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Exploiting external knowledge to improve video retrieval
Proceedings of the international conference on Multimedia information retrieval
Unsupervised multi-feature tag relevance learning for social image retrieval
Proceedings of the ACM International Conference on Image and Video Retrieval
Multi-source shared nearest neighbours for multi-modal image clustering
Multimedia Tools and Applications
News contextualization with geographic and visual information
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Enhancing news organization for convenient retrieval and browsing
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Multimedia search reranking: A literature survey
ACM Computing Surveys (CSUR)
Contextual object category recognition for RGB-D scene labeling
Robotics and Autonomous Systems
Hi-index | 0.00 |
Online photo sharing systems, such as Flickr and Picasa, provide a valuable source of human-annotated photos. Textual annotations are used not only to describe the visual content of an image, but also subjective, spatial, temporal and social dimensions, complicating the task of keyword-based search. In this paper we investigate a method that exploits visual annotations, e.g. notes in Flickr, to enhance keyword-based systems retrieval performance. For this purpose we adopt the bag-of-visual-words approach for content-based image retrieval as our baseline. We then apply rank aggregation of the top 25 results obtained with a set of visual annotations that match the keyword-based query. The results on retrieval experiments show significant improvements in retrieval performance when comparing the aggregated approach with our baseline, which also slightly outperforms text-only search. When using a textual filter on the search space in combination with the aggregated approach an additional boost in retrieval performance is observed, which underlines the need for large scale content-based image retrieval techniques to complement the text-based search.