Support vector machine learning for image retrieval

By Lei Zhang, Fuzong Lin, Bo Zhang. Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205), 2001

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Kernel Methods and Machine Learning (2014, unknown): “For a few examples, see [29, 35, 74, 89, 97, 103, 109, 113, 173, 184, 188, 211, 245, 256, 272, 273, 318].”
Manifold-ranking based image retrieval (2004, methodology): “3 Relevance Feedback When both positive and negative examples are provided by the user, we apply MRBIR (with three active learning methods), SVM [28] and SVMactive [25] to refine the retrieval result, and compare their performance.”
Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval (2006, methodology): “Many binary-classification algorithms were designed to treat the positive samples and negative samples equally [31], [27].”
A survey of content-based image retrieval with high-level semantics (2007, methodology): “and is considered a good candidate for learning in image retrieval system [35,69,81].”
A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback (2012, methodology): “These algorithms usually learn a classifier to separate the relevant and irrelevant training data and employ the decision values from the learned classifier as the ranking scores of multimedia data [28], [38].”
Applications of Support Vector Machines for Pattern Recognition: A Survey (2002, methodology): “SVM classifier was used to separate two classes of relevant images and irrelevant images in [36,34, 87 ].”