Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
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In recent years, the employment of user feedback information to improve the image retrieval precision has become a hot subject in the research field. But in traditional relevance feedback methods, both relevant and irrelevant user assigned information was required for the retrieval system. For the sake of practicality and convenience, the present paper advances that users only need to choose their inquired image files, which generate a new index vector as relevant information. Through the feature vector space transformation, the index is moved towards the user’s inquiry intention. Meanwhile, the analysis of the user’s inquiry intention together with relevant forecast of index target in the database make it possible for the less similar vectors to get closer to the demanding vectors and thus increasing index precision. In this paper, a prototype system is introduced of image database and experimental illustration to 51138 image files. Compared with the traditional relevance feedback technique, the suggested method is shown to obviously improve the retrieval function.