One-class SVM for learning in image retrieval

By Yunqiang Chen, Xiang Sean Zhou, Thomas S. Huang. 2001

763 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Ensemble of exemplar-SVMs for object detection and beyond (2011, methodology): “Each of these ExemplarSVMs is thus defined by a single positive instance and millions of negatives.”
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection (2018, methodology): “such as one-class SVM (Chen et al. (2001); Song et al.”
Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019, methodology): “We compare the proposed method with previous state-of-the-art methods on the KDDCUP dataset, including OC-SVM [35], a deep clustering network (DCN) [42], DSEBM [43], DAGMM [48] and the baseline variants of MemAE.”
Efficient GAN-Based Anomaly Detection (2018, methodology): “Popular techniques utilize clustering approaches or nearest neighbor methods (Xiong et al., 2011; Zimek et al., 2012) and one class classification approaches that learn a discriminative boundary around normal data, such as one-class SVMs (Yunqiang Chen & Huang, 2001).”
Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval (2006, methodology): “They either adjust the weights of various features to adapt to the user’s preferences [21] or estimate the density of the positive feedback examples [5].”
One-class classification: taxonomy of study and review of techniques (2013, neutral): “…Retrieval (Manevitz & Yousef, 2001); Missing Data/Data Correction (Juszczak & Duin, 2004; Xiaomu et al ., 2008); Image Database Retrieval (Chen et al ., 2001; Tax & Duin, 2001b; Gondra et al ., 2004; Seo, 2007); Face/Object Recognition Applications (Wang et al ., 2004; Bicego et al .,…”
Unsupervised Salience Learning for Person Re-identification (2013, methodology): “As shown in [6], the decision function of kernel one-class SVM can well capture the density and modality of feature distribution.”
Manifold-ranking based image retrieval (2004, background): “For example, the authors of [14] propose two query expansion approaches that selectively add relevant objects to the query, namely similar expansion and distant expansion; another approach adopted by [2] estimates the distribution of the target images in the feature space using one-class SVM.”
Image retrieval: Ideas, influences, and trends of the new age (2008, background): “This leads to a natural formulation of one-class SVM for learning relevant regions in the feature space from feedback [Chen et al. 2002].”
Kernel Methods in Computer Vision (2009, methodology): “The authors propose a one-class SVM setup to rank images for retrieval tasks based on user feedback [Chen et al., 2001].”
Multiresolution Knowledge Distillation for Anomaly Detection (2020, methodology): “23 OCSVM[14] 63.”
Learning Neural Random Fields with Inclusive Auxiliary Generators (2018, methodology): “One-Class Support Vecter Machine (OC-SVM) [54] and Support Vector Data Description (SVDD) [55].”
Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning (2021, background): “Such diversity makes it difﬁcult to apply anomaly detection methods for attribute-only data (e.g., OC-SVM [14]) or plain networks (e.g., LOF [15]) to attributed networks directly.”
Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network (2020, background): “While they have been successfully used in many real-world applications [3, 15, 32], they are often limited to data-rich scenarios so that the normal patterns can be sufﬁciently captured.”