Support Vector Machines for Texture Classification

By Kwang In Kim, Keechul Jung, Se Hyun Park, Hang-Joon Kim. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002

374 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/025bcbb4913c1ac4c6ec511369c5e62079322e48

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Citations in the corpus

Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy (2010, methodology): “The rationale for developing a domain adaptation technique in the framework of SVMs [12], [13] is due to the effectiveness of this classification methodology that attempts to separate samples belonging to different classes by defining maximum margin hyperplanes [14], [15], [16].”
A Statistical Approach to Texture Classification from Single Images (2005, background): “For instance, Support Vector Machines (Cristianini and Shawe-Taylor, 2000; Hayman et al., 2004; Kim et al., 2002; Schólkopf and Smola, 2002), and perhaps more appropriately Relevance Vector Machines (Tipping, 2001), are both capable of reducing the number of models while providing good generalisatio”
Mining the customer credit using classification and regression tree and multivariate adaptive regression splines (2006, methodology): “It has recently been successfully applied to machine learning and pattern recognition such as face detection (Osuna et al., 1997), text categorization (Joachims, 1998; Dumais et al., 1998), and texture classification (Kim et al., 2002) problems.”
From BoW to CNN: Two Decades of Texture Representation for Texture Classification (2018, methodology): “Many texture representation approaches exploit the Brodatz database for evaluations [99, 125, 163, 187, 190, 231], however in most cases the entire database is not utilized, except in some recent studies [67, 110, 132, 182, 263].”
A Statistical Approach to Texture Classification from Single Images (2004, background): “For instance, Support Vector Machines (Cristianini and Shawe-Taylor, 2000; Hayman et al., 2004; Kim et al., 2002; Schólkopf and Smola, 2002), and perhaps more appropriately Relevance Vector Machines (Tipping, 2001), are both capable of reducing the number of models while providing good generalisatio”
On the Significance of Real-World Conditions for Material Classification (2004, methodology): “Most work on texture recognition [21, 23, 14] has dealt with planar image patches sampled, for instance, from the Brodatz collection [4].”
A study of Gaussian mixture models of color and texture features for image classification and segmentation (2006, methodology): “These include Bayes classifiers assuming multivariate Gaussian distributions for the features [3–6]; Fisher transformation [7,8]; nonparametric nearestneighbor classification [9–12]; classification trees [8]; learning vector quantization [13,14]; feed-forward neural networks [15]; and recently suppo”