Image annotation using SVM

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Content-based visual search learned from social media (2012, methodology): “The model-based approaches, often conducted in a supervised learning framework, focus on learning a mapping or projection between low-level visual features and high-level semantic concepts given a number of training examples [8, 18, 24, 57, 91].”
A review on automatic image annotation techniques (2012, methodology): “[23] use SVM to do image segmentation and classification simultaneously.”
Quantifying the Visual Concreteness of Words and Topics in Multimodal Datasets (2018, methodology): “Some commonly used datasets couple images with a handful of un-ordered tags (Barnard et al., 2003; Cusano et al., 2004; Grangier and Bengio, 2008; Chen et al., 2013, inter alia) or short, literal natural language captions (Farhadi et al., 2010; Ord´o˜nez et al., 2011; Kulkarni et al., 2013; Fang et…”
TagProp: Discriminative metric learning in nearest neighbor models for image auto-annotation (2009, unknown): “Therefore, discriminative models for tag prediction have also been proposed [3, 7, 10].”