Information embedding based on user's relevance feedback for image retrieval
Catherine S. Lee, Wei‐Ying Ma, HongJiang Zhang · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1999
An image retrieval system based on an information embedding scheme is proposed. Using relevance feedback, the system gradually embeds correlations between images from a high- level semantic perspective. The system starts with low-level image features and acquires knowledge from users to correlate different images in the database. Through the selection of positive and negative examples based on a given query, the semantic relationships between images are captured and embedded into the system by splitting/merging image clusters and updating the correlation matrix. Image retrieval is then based on the resulting image clusters and the correlation matrix obtained through relevance feedback.