Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Comparing images using color coherence vectors
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Learning-based linguistic indexing of pictures with 2--d MHMMs
Proceedings of the tenth ACM international conference on Multimedia
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
The Journal of Machine Learning Research
A bootstrapping approach to annotating large image collection
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Autonomous visual model building based on image crawling through internet search engines
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Probabilistic web image gathering
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
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In this paper, we provide a novel image annotation model by mining the Web. In our approach, the concepts or words appearing in the associated text are extracted and filtered as the semantic annotations for the corresponding Web images. In order to alleviate the influence caused by the noise images, for each semantic concept, we improve Web image-word relationships using Mixture Gaussian Distribution Model. By doing so, the concepts or words relevant to any image are re-weighed by both considering their relevance to the image in term of text and in term of visual feature. In fact, all the words associated to an image are not semantically independent. We use co-occurrences between two words to describe their semantic relevance. Thus, we further use a method, called Word Promotion, to co-enhance the weights of all the words associated to a given image based on their co-occurrences. Our experiments are conducted in several ways and the results show that our annotation method can achieve a satisfactory performance.