WordNet: a lexical database for English
Communications of the ACM
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
Effective automatic image annotation via a coherent language model and active learning
Proceedings of the 12th annual ACM international conference on Multimedia
Real-time computerized annotation of pictures
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Real-World Image Annotation and Retrieval: An Introduction to the Special Section
IEEE Transactions on Pattern Analysis and Machine Intelligence
Photo-based question answering
MM '08 Proceedings of the 16th ACM international conference on Multimedia
WordNet::Similarity: measuring the relatedness of concepts
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
A text-to-picture synthesis system for augmenting communication
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Multimedia search with pseudo-relevance feedback
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
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An image is worth of thousand words. Automatic web image annotation is a practical and effective way for both web image retrieval and image understanding. However, current annotation techniques are very difficult to get natural language interpretation for images such as "pandas eat bamboo". In this paper, we proposed an approach to interpret image semantics through semi-supervised mining annotated words. The idea in this approach mainly consists of three parts: at first, the visibility of annotated words of target image is calculated by semi-supervised learning approach from the landmark words in WordNet; then the annotated words are used as queries to retrieve matched web pages; at last, the meaningful sentences in the matched web pages are ranked as the interpretation of target image by semi-supervised learning approach. Experiments conducted on real-world web images demonstrate the effectiveness of the proposed approach.