کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4969526 | 1449977 | 2017 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Graph regularized nonnegative sparse coding using incoherent dictionary for approximate nearest neighbor search
ترجمه فارسی عنوان
نمودار برنامه نویسی ناسازگار ضعیف را با استفاده از فرهنگ لغت نامحدود برای نزدیکترین جستجوی همسایگی تنظیم می کند
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کلمات کلیدی
برنامه نویسی غیر مجاز، یادگیری فرهنگ لغت نابجا، مقررات لاپلایسی، تقریبا نزدیکترین همسایگی جستجو، بازیابی تصویر،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
In this paper, we consider the problem of approximate nearest neighbor (ANN) retrieval with the method of sparse coding, which is a promising tool of discovering compact representation of high-dimensional data. A new study, exploiting the indices of the active set of sparse coded data as retrieval code, exhibits an appealing ANN route. Here our work aims to enhance the method via considering its shortages of the local structure of the data. Our primary innovation is two-fold: We introduce the graph Laplacian regularization to preserve the local structure of the original data into reduced space, which is indeed beneficial to ANN. And we impose non-negativity constraints such that the retrieval code can more effectively reflect the neighborhood relation, thereby cutting down on unnecessary hash collision. To this end, we learn an incoherent dictionary with both rules via a novel formulation of sparse coding. The resulting optimization problem can be provided with an available solution by the widely used iterative scheme, where we resort to the feature-sign search algorithm in the sparse coding step and exploit the method that uses a Lagrange dual for dictionary learning step. Experimental results on publicly available image data sets manifest that the rules are positive to promote the classification and ANN accuracies. Compared with several state-of-the-art ANN techniques, our methods can achieve an interesting improvement in retrieval accuracy.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 70, October 2017, Pages 75-88
Journal: Pattern Recognition - Volume 70, October 2017, Pages 75-88
نویسندگان
Zhang Yupei, Xiang Ming, Yang Bo,