A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
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
The Journal of Machine Learning Research
On image auto-annotation with latent space models
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
A Sparse Support Vector Machine Approach to Region-Based Image Categorization
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Formulating Semantic Image Annotation as a Supervised Learning Problem
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Region based image annotation through multiple-instance learning
Proceedings of the 13th annual ACM international conference on Multimedia
Using Multiple Segmentations to Discover Objects and their Extent in Image Collections
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
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
Automatic image annotation via local multi-label classification
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Exploring Flickr's related tags for semantic annotation of web images
Proceedings of the ACM International Conference on Image and Video Retrieval
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Automatic image annotation techniques that try to identify the objects in images usually need the images to be segmented first, especially when specifically annotating image regions. The purpose of segmentation is to separate different objects in images from each other, so that objects can be processed as integral individuals. Therefore, annotation performance is highly influenced by the effectiveness of segmentation. Unfortunately, automatic segmentation is a difficult problem, and most of the current segmentation techniques do not guarantee good results. A multiple segmentations algorithm is proposed by Russell et al. [12] to discover objects and their extent in images. In this paper, we explore the novel use of multiple segmentations in the context of image auto-annotation. It is incorporated into a region based image annotation technique proposed in previous work, namely the training image based feature space approach. Three different levels of segmentations were generated for a 5000 image collection. Experimental results show that image auto-annotation achieves better performance when using all three segmentation levels together than using any single one on its own.