Contextual tag inference

This article examines the use of two kinds of context to improve the results of content-based music taggers: the relationships between tags and between the clips of songs that are tagged. We show that users agree more on tags applied to clips temporally “closer” to one another; that conditional restricted Boltzmann machine models of tags can more accurately predict related tags when they take context into account; and that when training data is “smoothed” using context, support vector machines can better rank these clips according to the original, unsmoothed tags and do this more accurately than three standard multi-label classifiers.

[1]  Rong Jin,et al.  Correlated Label Propagation with Application to Multi-label Learning , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[2]  Zhi-Hua Zhou,et al.  ML-KNN: A lazy learning approach to multi-label learning , 2007, Pattern Recognit..

[3]  Fei-Fei Li,et al.  Modeling mutual context of object and human pose in human-object interaction activities , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  Grigorios Tsoumakas,et al.  Random k -Labelsets: An Ensemble Method for Multilabel Classification , 2007, ECML.

[5]  Yueting Zhuang,et al.  Multi-Task Sparse Discriminant Analysis (MtSDA) with Overlapping Categories , 2010, AAAI.

[6]  Ryan M. Rifkin,et al.  Musical query-by-description as a multiclass learning problem , 2002, 2002 IEEE Workshop on Multimedia Signal Processing..

[7]  Grigorios Tsoumakas,et al.  Mining Multi-label Data , 2010, Data Mining and Knowledge Discovery Handbook.

[8]  Nuno Vasconcelos,et al.  Holistic context modeling using semantic co-occurrences , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  MenczerFilippo,et al.  Contextual tag inference , 2011 .

[10]  Geoffrey E. Hinton,et al.  Restricted Boltzmann machines for collaborative filtering , 2007, ICML '07.

[11]  David J. Hand,et al.  Measuring classifier performance: a coherent alternative to the area under the ROC curve , 2009, Machine Learning.

[12]  Grigorios Tsoumakas,et al.  MULAN: A Java Library for Multi-Label Learning , 2011, J. Mach. Learn. Res..

[13]  Geoffrey E. Hinton,et al.  Modeling Human Motion Using Binary Latent Variables , 2006, NIPS.

[14]  Andrea Vedaldi,et al.  Objects in Context , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[15]  Daniel P. W. Ellis,et al.  Please Scroll down for Article Journal of New Music Research a Web-based Game for Collecting Music Metadata a Web-based Game for Collecting Music Metadata , 2022 .

[16]  David A. Forsyth,et al.  Utility data annotation with Amazon Mechanical Turk , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[17]  Grigorios Tsoumakas,et al.  Multi-Label Classification of Music into Emotions , 2008, ISMIR.

[18]  Daphne Koller,et al.  Learning Spatial Context: Using Stuff to Find Things , 2008, ECCV.

[19]  Jin Ha Lee,et al.  Crowdsourcing Music Similarity Judgments using Mechanical Turk , 2010, ISMIR.

[20]  Malcolm Slaney,et al.  Semantic-audio retrieval , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[21]  Nuno Vasconcelos,et al.  Holistic context modeling using semantic co-occurrences , 2009, CVPR.

[22]  Geoffrey E. Hinton Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

[23]  Javier R. Movellan,et al.  Whose Vote Should Count More: Optimal Integration of Labels from Labelers of Unknown Expertise , 2009, NIPS.

[24]  Paul Smolensky,et al.  Information processing in dynamical systems: foundations of harmony theory , 1986 .

[25]  Riccardo Miotto,et al.  Improving Auto-tagging by Modeling Semantic Co-occurrences , 2010, ISMIR.

[26]  Ivor W. Tsang,et al.  Tag-based web photo retrieval improved by batch mode re-tagging , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[27]  Thierry Bertin-Mahieux,et al.  Automatic Generation of Social Tags for Music Recommendation , 2007, NIPS.

[28]  Thierry Bertin-Mahieux,et al.  Autotagger: A Model for Predicting Social Tags from Acoustic Features on Large Music Databases , 2008 .

[29]  Alexei A. Efros,et al.  Putting Objects in Perspective , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[30]  Christopher D. Manning,et al.  Introduction to Information Retrieval , 2010, J. Assoc. Inf. Sci. Technol..

[31]  Brendan T. O'Connor,et al.  Cheap and Fast – But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks , 2008, EMNLP.

[32]  Yoshua Bengio,et al.  Classification using discriminative restricted Boltzmann machines , 2008, ICML '08.

[33]  J. Besag Statistical Analysis of Non-Lattice Data , 1975 .

[34]  François Pachet,et al.  Signal + Context = Better Classification , 2007, ISMIR.

[35]  Mehryar Mohri,et al.  AUC Optimization vs. Error Rate Minimization , 2003, NIPS.

[36]  Douglas Eck,et al.  Learning Tags that Vary Within a Song , 2010, ISMIR.

[37]  Youngmoo E. Kim,et al.  Exploring automatic music annotation with "acoustically-objective" tags , 2010, MIR '10.

[38]  Antonio Torralba,et al.  Using the Forest to See the Trees: A Graphical Model Relating Features, Objects, and Scenes , 2003, NIPS.

[39]  Razvan Pascanu,et al.  Autotagging music with conditional restricted Boltzmann machines , 2011, ArXiv.

[40]  Ciro Cattuto,et al.  Evaluating similarity measures for emergent semantics of social tagging , 2009, WWW '09.

[41]  Newton Lee ACM Transactions on Multimedia Computing, Communications and Applications (ACM TOMCCAP) , 2007, CIE.

[42]  Rossano Schifanella,et al.  Folks in Folksonomies: social link prediction from shared metadata , 2010, WSDM '10.

[43]  Jiebo Luo,et al.  Learning multi-label scene classification , 2004, Pattern Recognit..