Learning multi-label scene classification

By Matthew R. Boutell, Jiebo Luo, Xipeng Shen, Christopher M. Brown. Pattern Recognition, 2004

2,528 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/896b9c1551b7ffa347baed144582ec3b5d88f703

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Learning Spatial Regularization with Image-Level Supervisions for Multi-label Image Classification (2017, background): “Multi-label image classification is an important task in computer vision with various applications, such as scene recognition [4, 30, 31], multi-object recognition [25, 19, 18], human attribute recognition [24], etc.”
Learning Tags from Unsegmented Videos of Multiple Human Actions (2011, methodology): “We compare the performance of our model against two existing approaches: multi-label SVM [6] and CorrLDA [4].”
Addressing imbalance in multilabel classification: Measures and random resampling algorithms (2015, methodology): “Among the transformation methods, the most popular are those based on the binarization of the MLD, such as Binary Relevance (BR) [11] and Ranking by Pairwise Comparison [12], and the LP [13] transformation, which produces a multiclass dataset from an MLD considering each labelset as class.”
Large Margin Metric Learning for Multi-Label Prediction (2015, background): “For instance, a document can be associated with a range of topics, such as News, Finance and Sports (Schapire and Singer 2000); a gene belongs to the functions of protein synthesis, metabolism and transcription (Barutcuoglu, Schapire, and Troyanskaya 2006); an image may have both beach and urban tag”
Hypergraph spectral learning for multi-label classification (2008, methodology): “Such type of problems occurs in many important applications, such as protein function classification [9], text categorization [25], and semantic scene classification [4].”
Mining Multi-label Data (2010, background): “179 15 [1] yeast biology 2417 0 103 14 4.”
Feature selection for multi-label classification using multivariate mutual information (2013, background): “Multi-label classification is a challenging problem that emerges in several modern applications such as text categorization, gene function classification, and semantic annotation of images (Schapire and Singer, 2000; Sebastiani, 2002; Lewis et al., 2004; Diplaris et al., 2005; Boutell et al., 2004).”
Binary relevance for multi-label learning: an overview (2018, background): “Here, each binary classification problem corresponds to one class label in the label space Y [9].”
Classifier chains for multi-label classification (2009, methodology): “2008), scene and video classification (Boutell et al. 2004; Dimou et al. 2009), and bioinformatics (Elisseeff and Weston 2001; Vens et al.”
Order-Free RNN with Visual Attention for Multi-Label Classification (2017, background): “…vision, a variety of applications benefit from the development and success of multi-label classification algorithms (Zhang and Zhou 2014; Boutell et al. 2004; Schapire and Singer 2000; Godbole and Sarawagi 2004; Lin et al. 2014; Kang et al. 2016b; 2016a; Boutell et al. 2004; Shao et al. 2016).”
Multi-label learning with millions of labels: recommending advertiser bid phrases for web pages (2013, background): “Almost all the multi-label classifiers that have been proposed in the literature have prediction costs that are at least linear in the number of labels [8,19–22,24,38–42,47].”
Training structural SVMs when exact inference is intractable (2008, background): “Four real datasets, Scene (Boutell et al., 2004), Yeast (Elisseeff & Weston, 2002), Reuters (the RCV1 subset 1 data set) (Lewis et al.”
Feature selection for multi-label naive Bayes classification (2009, methodology): “Although the generality of multi-label problems inevitably makes it more difficult to learn, researchers have proposed a number of algorithms to learn from multi-label instances, such as multi-label decision trees [8,9], multi-label neural networks [10,45], and multi-label kernel methods [2,12,17,23”
Sparse Bayesian Multi-Task Learning (2011, methodology): “In this section, we evaluate the proposed model on two data sets: Yeast [17] and Scene [9], which have been widely used as testbeds to evaluate multi-task learning approaches [28, 29, 15].”
Multi-label learning by exploiting label dependency (2010, methodology): “As shown in Table 7, for data sets with regular number of examples (|S| < 5000), Lead is significantly superior to the compared algorithms in 31.4% (Bsvm), 31.4% (Ml-knn), 68.6% (Bp-mll) and 54.3% (Ecc) cases, and is inferior to them in much less 0.0% (Bsvm), 17.1% (Mlknn), 8.6% (Bp-mll) and 17.1% (”