Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Automatic multimedia cross-modal correlation discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A Hierarchical Field Framework for Unified Context-Based Classification
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Efficient MAP approximation for dense energy functions
ICML '06 Proceedings of the 23rd international conference on Machine learning
Multiple Bernoulli relevance models for image and video annotation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Automated image annotation using global features and robust nonparametric density estimation
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
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In this work we employ contextual information to improve the quality of image labellings provided by an existing automatic image annotation algorithm in a weakly supervised setting, where each training image is labelled but it is not known which part of the image its labels are referring to. We recast the problem into that of constructing a graph which encodes pairwise consistency of candidate annotations and observe that mutually consistent labels will form a compact cluster in this graph. We recover the clusters using a spectral theory based technique. The results are demonstrated on the Corel5k dataset. With improvements in the range of 25%- 55% the performance in some cases approaches the state of the art despite using a very simple base algorithm.