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

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Standard

A k-nearest neighbor multilabel ranking algorithm with application to content-based image retrieval. / Zhang, Honglei; Kiranyaz, Serkan; Gabbouj, Moncef.

2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings. IEEE, 2017. p. 2587-2591.

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

Harvard

Zhang, H, Kiranyaz, S & Gabbouj, M 2017, A k-nearest neighbor multilabel ranking algorithm with application to content-based image retrieval. in 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings. IEEE, pp. 2587-2591, IEEE International Conference on Acoustics, Speech and Signal Processing, 1/01/00. https://doi.org/10.1109/ICASSP.2017.7952624

APA

Zhang, H., Kiranyaz, S., & Gabbouj, M. (2017). A k-nearest neighbor multilabel ranking algorithm with application to content-based image retrieval. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings (pp. 2587-2591). IEEE. https://doi.org/10.1109/ICASSP.2017.7952624

Vancouver

Zhang H, Kiranyaz S, Gabbouj M. A k-nearest neighbor multilabel ranking algorithm with application to content-based image retrieval. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings. IEEE. 2017. p. 2587-2591 https://doi.org/10.1109/ICASSP.2017.7952624

Author

Zhang, Honglei ; Kiranyaz, Serkan ; Gabbouj, Moncef. / A k-nearest neighbor multilabel ranking algorithm with application to content-based image retrieval. 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings. IEEE, 2017. pp. 2587-2591

Bibtex - Download

@inproceedings{f5cee32ff6f84de399358a2329655fd6,
title = "A k-nearest neighbor multilabel ranking algorithm with application to content-based image retrieval",
abstract = "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.",
keywords = "Content-Based Image Retrieval, k-Nearest Neighbor, Multilabel Learning",
author = "Honglei Zhang and Serkan Kiranyaz and Moncef Gabbouj",
year = "2017",
month = "6",
day = "16",
doi = "10.1109/ICASSP.2017.7952624",
language = "English",
publisher = "IEEE",
pages = "2587--2591",
booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - A k-nearest neighbor multilabel ranking algorithm with application to content-based image retrieval

AU - Zhang, Honglei

AU - Kiranyaz, Serkan

AU - Gabbouj, Moncef

PY - 2017/6/16

Y1 - 2017/6/16

N2 - 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.

AB - 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.

KW - Content-Based Image Retrieval

KW - k-Nearest Neighbor

KW - Multilabel Learning

U2 - 10.1109/ICASSP.2017.7952624

DO - 10.1109/ICASSP.2017.7952624

M3 - Conference contribution

SP - 2587

EP - 2591

BT - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings

PB - IEEE

ER -