Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
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
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
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
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Automatic multimedia cross-modal correlation discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
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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We propose a concept-centered approach that combines region- and image-level analysis for automatic image annotation (AIA). At the region level, we group regions into separate concept groups and perform concept-centered region clustering separately. The key idea is that we make use of the inter- and intra-concept region distribution to eliminate unreliable region clusters and identify the main region clusters for each concept. We then derive the correspondence between the image region clusters and concepts. To further enhance the accuracy of AIA task, we employ a multi-stage kNN classification using the global features at the image level. Finally, we perform fusion of region- and image-level analysis to obtain the final annotations. Our results have been found to improve the performance significantly, with gains of 18.5% in recall and 8.3% in “number of concepts detected”, as compared to the best reported AIA results for the Corel image data set.