Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
An Integrative Semantic Framework for Image Annotation and Retrieval
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Improve Image Annotation by Combining Multiple Models
SITIS '07 Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System
Effective Image Retrieval Based on Hidden Concept Discovery in Image Database
IEEE Transactions on Image Processing
Extracting semantics from audio-visual content: the final frontier in multimedia retrieval
IEEE Transactions on Neural Networks
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Automatic image annotation is an important and useful approach to narrow the semantic gap between visual features and semantics. However, it is time-consuming job since it extracting the visual features from a whole image to learn the relationship between low-level features and high-level semantic. In this paper, an image annotation method based on central region features reduction is proposed. Differ from the traditional annotation approach based on the whole image features, the proposed method analyze the central area which associate with the image semantics and only vision features of the area are extracted, then feature reduction based on Rough Set is used for getting the relationship between image visual features and semantics, lastly image annotation is executed. The experimental results show that the proposed method is effective and useful.