An Iterative Partitioning-Based Method for Semi-Supervised Annotation Learning in Image Collections

Rene Grzeszick, Gernot A. Fink
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
more » ... is introduced that is based on partitioning the data in a multi-view approach. The method is applied to large, diverse image collections of natural scene images. Experiments are performed on the 15 Scenes and SUN databases. It is shown that for sparsely labeled datasets the proposed annotation learning algorithm is able to infer additional knowledge from the unlabeled samples and therefore improve the performance of visual classifiers in comparison to supervised learning. Furthermore, the proposed algorithm outperforms other related semi-supervised learning approaches.
doi:10.1142/s0218001416550053 fatcat:7se2zlj4dzhhvjtqt342zbmzfm