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AuthorZhang H.
AuthorKiranyaz, Mustafa Serkan
AuthorGabbouj M.
Available date2022-04-26T12:31:22Z
Publication Date2017
Publication NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/ICASSP.2017.7952624
URIhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85023746875&doi=10.1109%2fICASSP.2017.7952624&partnerID=40&md5=0a8ea4962b001df20c88e7ff1d0c9b6b
URIhttp://hdl.handle.net/10576/30625
AbstractMultilabel ranking is an important machine learning task with many applications, such as content-based image retrieval (CBIR). However, when the number of labels is large, traditional algorithms are either infeasible or show poor performance. In this paper, we propose a simple yet effective multilabel ranking algorithm that is based on k-nearest neighbor paradigm. The proposed algorithm ranks labels according to the probabilities of the label association using the neighboring samples around a query sample. Different from traditional approaches, we take only positive samples into consideration and determine the model parameters by directly optimizing ranking loss measures. We evaluated the proposed algorithm using four popular multilabel datasets. The proposed algorithm achieves equivalent or better performance than other instance-based learning algorithms. When applied to a CBIR system with a dataset of 1 million samples and over 190 thousand labels, which is much larger than any other multilabel datasets used earlier, the proposed algorithm clearly outperforms the competing algorithms.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectContent-Based Image Retrieval
k-Nearest Neighbor
Multilabel Learning
TitleA k-nearest neighbor multilabel ranking algorithm with application to content-based image retrieval
TypeConference Paper
Pagination2587-2591
dc.accessType Abstract Only


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