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An Iterative Partitioning-Based Method for Semi-Supervised Annotation Learning in Image Collections
2016
International journal of pattern recognition and artificial intelligence
Labeling images is tedious and costly work that is required for many applications, for example, tagging, grouping and exploring of image collections. It is also necessary for training visual classifiers that recognize scenes or objects. It is therefore desirable to either reduce the human effort or infer additional knowledge by addressing this task with algorithms that allow for learning image annotations in a semi-supervised manner. In this paper, a semi-supervised annotation learning
doi:10.1142/s0218001416550053
fatcat:7se2zlj4dzhhvjtqt342zbmzfm