SemQuery: Semantic Clustering and Querying on Heterogeneous Features for Visual Data
IEEE Transactions on Knowledge and Data Engineering
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
Image Retrieval Using Multiple Evidence Ranking
IEEE Transactions on Knowledge and Data Engineering
Image annotations by combining multiple evidence & wordNet
Proceedings of the 13th annual ACM international conference on Multimedia
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Automatic image annotation techniques are proposed for overcoming the so-called semantic-gap between image low-level feature and high-level concept in content-based image retrieval systems. Due to the limitations of techniques, current state-of-the-art automatic image annotation models still produce some irrelevant concepts to image semantics, which are an obstacle to getting high-quality image retrieval. In this paper we focus on improving image annotation to facilitate web image retrieval. The novelty of our work is to use both WordNet and textual information in web documents to refine original coarse annotations produced by the classic Continuous Relevance Model (CRM). Each keyword in annotations is associated with a certain weight, and larger the weight is, more related to image semantics the corresponding concept is. The experimental results show that the refined annotations improve image retrieval to some extent, compared to the original coarse annotations.