کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6939085 | 1449968 | 2018 | 42 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Cross-Modal Discrete Hashing
ترجمه فارسی عنوان
هسینگ گسسته متقاطع
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کلمات کلیدی
بازیابی چندرسانه ای، هش متقابل بهینه سازی گسسته،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
In this paper, we present a new cross-modal discrete hashing (CMDH) approach to learn compact binary codes for cross-modal multimedia search. Unlike most existing cross-modal hashing methods which usually relax the optimization objective function to obtain hash codes, we develop a discrete optimization framework to jointly learn binary codes and a series of hash functions for each modality, so that the performance drop due to the inferior optimization techniques can be avoided. Specifically, we present two cross-modal hashing algorithms called CMDH-linear and CMDH-kernel under the proposed framework, which performs linear and non-linear mappings to learn binary codes, respectively. Different from existing cross-modal hashing methods which maximize the corrections of hash codes from different modalities, our CMDH learns a set of shared binary codes for samples captured from different modalities, so that the modality gap can be effectively removed in cross-modal multimedia retrieval. To further improve the flexibility of our approach for different scenarios, we extend CMDH to unsupervised CMDH (unCMDH) and discrete multi-modal hashing (MMDH), which learns hash codes for training data without label information and with multi-modal labelled data. Experimental results on three benchmark datasets clearly show that our methods achieve competitive results with the state-of-the-arts.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 79, July 2018, Pages 114-129
Journal: Pattern Recognition - Volume 79, July 2018, Pages 114-129
نویسندگان
Venice Erin Liong, Jiwen Lu, Yap-Peng Tan,