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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 degenerationdoi:10.9756/bijaip.1001 fatcat:hzdw53v42fhtvckwsghnv24ura