Competitive Quantization for Approximate Nearest Neighbor Search
المؤلف | Ozan, Ezgi Can |
المؤلف | Kiranyaz, Serkan |
المؤلف | Gabbouj, Moncef |
تاريخ الإتاحة | 2021-09-05T05:40:11Z |
تاريخ النشر | 2016 |
اسم المنشور | IEEE Transactions on Knowledge and Data Engineering |
المصدر | Scopus |
الرقم المعياري الدولي للكتاب | 10414347 |
الملخص | In this study, we propose a novel vector quantization algorithm for Approximate Nearest Neighbor (ANN) search, based on a joint competitive learning strategy and hence called as competitive quantization (CompQ). CompQ is a hierarchical algorithm, which iteratively minimizes the quantization error by jointly optimizing the codebooks in each layer, using a gradient decent approach. An extensive set of experimental results and comparative evaluations show that CompQ outperforms the-state-of-the-art while retaining a comparable computational complexity. |
اللغة | en |
الناشر | IEEE Computer Society |
الموضوع | Approximate nearest neighbor search binary codes large-scale retrieval vector quantization |
النوع | Article |
الصفحات | 2884-2894 |
رقم العدد | 11 |
رقم المجلد | 28 |
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