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A k-nearest neighbor multilabel ranking algorithm with application to content-based image retrieval

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Original languageEnglish
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
Number of pages5
ISBN (Electronic)9781509041176
Publication statusPublished - 16 Jun 2017
Publication typeA4 Article in a conference publication
EventIEEE International Conference on Acoustics, Speech and Signal Processing -
Duration: 1 Jan 19001 Jan 2000

Publication series

ISSN (Electronic)2379-190X


ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing


Multilabel 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.


  • Content-Based Image Retrieval, k-Nearest Neighbor, Multilabel Learning

Publication forum classification

Field of science, Statistics Finland