Information retrieval using a singular value decomposition model of latent semantic structure
SIGIR '88 Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 1998 conference on Advances in neural information processing systems II
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Query by Tapping: A New Paradigm for Content-Based Music Retrieval from Acoustic Input
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Towards musical query-by-semantic-description using the CAL500 data set
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Multiple Bernoulli relevance models for image and video annotation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A General Framework of Progressive Filtering and Its Application to Query by Singing/Humming
IEEE Transactions on Audio, Speech, and Language Processing
Semantic Annotation and Retrieval of Music and Sound Effects
IEEE Transactions on Audio, Speech, and Language Processing
Innovative Internet video consuming based on media analysis techniques
Electronic Commerce Research
Multimedia analysis techniques for e-learning
International Journal of Learning Technology
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Query-by-semantic-description (QBSD) is a natural way for searching/annotating music in a large database. To improve QBSD, we propose the use of anti-words for each annotation word based on the concept of supervised multiclass labeling (SML). More specifically, words that are highly associated with the opposite semantic meaning of a word constitute its anti-word set. By modeling both a word and its anti-word set, our annotation system can achieve 31.1% of equal mean per-word precision and recall, while the original SML model achieves 27.8%. Moreover, by constructing the models of the anti-word explicitly, the performance is also significantly improved for the retrieval system, especially when the query keyword is the antonym of an existing annotation word.