Abstract
While people compare images using semantic concepts, computers compare images using low-level visual features that sometimes have little to do with these semantics. To reduce the gap between the high-level semantics of visual objects and the low-level features extracted from them, in this paper we develop a framework of learning pseudo metrics (LPM) using neural networks for semantic image classification and retrieval. Performance analysis and comparative studies, by experimenting on an image database, show that the LPM has potential application to multimedia information processing.
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Wang, D., Ma, X. & Kim, Ys. Learning Pseudo Metric for Intelligent Multimedia Data Classification and Retrieval. J Intell Manuf 16, 575–586 (2005). https://doi.org/10.1007/s10845-005-4363-1
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DOI: https://doi.org/10.1007/s10845-005-4363-1