Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
PLSA-based image auto-annotation: constraining the latent space
Proceedings of the 12th annual ACM international conference on Multimedia
Modeling Semantic Aspects for Cross-Media Image Indexing
IEEE Transactions on Pattern Analysis and Machine Intelligence
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multimodal pLSA on visual features and tags
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
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Utilization of web images with social tags as training data has been a major trend for the development of automatic image tagging/classification systems. While the amount of information available on web sites such as Flickr is abundant, the majority of information obtained from such sites is in English, the dominant language on the web. This linguistic unbalance is expected to affect auto-tagging results in a negative way for non-English users, who demand image tags in their native languages. The objective of this research is to develop an image auto-tagging system which can generate tags in languages other than English. This paper examines the effect of linguistic unbalance in training data to construct auto-tagging systems, which aim to generate tags in minor languages. Furthermore, we propose methods which utilize an auto-tagging model generated on English training data, and convert the auto-tagging results to the target language. Subjective evaluations show that the proposed method is capable of generating auto-tagging results with better quality than conventional approaches.