Abstract
In this paper, we propose the MIML (Multi- Instance Multi-Label learning) framework which is associated with multiple class labels for Image Annotation. Compared to traditional learning frameworks, the MIML framework is more convenient and natural for representing complicated objects which have multiple semantic meanings. To learn from MIML examples we have taken a survey on MIML Boost, MIMLSVM, D-MIMLSVM, InsDif and SubCod algorithms. MIML Boost and MIML SVM are based on a simple degeneration strategy. Experiments based on this algorithm shows that solving problems involving complicated objects with multiple semantic meanings in the MIML framework can lead to good performance. As the degeneration process may lose information, we have considered D-MimlSvm algorithm which tackles MIML problems directly in a regularization framework. InsDif and SubCod algorithms works by transforming single-instances into the MIML representation for learning, while SubCod works by transforming single-label examples into the MIML representation for learning. We have compared the results of all the algorithms and have identified that InsDif framework leads to good performance rates.
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