Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning-based linguistic indexing of pictures with 2--d MHMMs
Proceedings of the tenth ACM international conference on Multimedia
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
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
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
PLSA-based image auto-annotation: constraining the latent space
Proceedings of the 12th annual ACM international conference on Multimedia
Using One-Class and Two-Class SVMs for Multiclass Image Annotation
IEEE Transactions on Knowledge and Data Engineering
Image annotations by combining multiple evidence & wordNet
Proceedings of the 13th annual ACM international conference on Multimedia
Active semi-supervised fuzzy clustering for image database categorization
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
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The goal of this paper is to study the image-concept relationship as it pertains to image annotation. We demonstrate how automatic annotation of images can be implemented on partially annotated databases by learning image-concept relationships from positive examples via inter-query learning. Latent semantic analysis (LSA), a method originally designed for text retrieval, is applied to an image/session matrix where relevance feedback examples are collected from a large number of artificial queries (sessions). Singular value decomposition (SVD) is exploited during LSA to propagate image annotations using only relevance feedback information. We will show how SVD can be used to filter a noisy image/session matrix and reconstruct missing values.