عرض بسيط للتسجيلة

المؤلفZhang H.
المؤلفKiranyaz, Mustafa Serkan
المؤلفGabbouj M.
تاريخ الإتاحة2022-04-26T12:31:22Z
تاريخ النشر2017
اسم المنشورICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
المصدرScopus
المعرّفhttp://dx.doi.org/10.1109/ICASSP.2017.7952624
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85023746875&doi=10.1109%2fICASSP.2017.7952624&partnerID=40&md5=0a8ea4962b001df20c88e7ff1d0c9b6b
معرّف المصادر الموحدhttp://hdl.handle.net/10576/30625
الملخصMultilabel 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.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعContent-Based Image Retrieval
k-Nearest Neighbor
Multilabel Learning
العنوانA k-nearest neighbor multilabel ranking algorithm with application to content-based image retrieval
النوعConference Paper
الصفحات2587-2591
dc.accessType Abstract Only


الملفات في هذه التسجيلة

الملفاتالحجمالصيغةالعرض

لا توجد ملفات لها صلة بهذه التسجيلة.

هذه التسجيلة تظهر في المجموعات التالية

عرض بسيط للتسجيلة