کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4969363 1449932 2017 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Massive parallelization of approximate nearest neighbor search on KD-tree for high-dimensional image descriptor matching
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Massive parallelization of approximate nearest neighbor search on KD-tree for high-dimensional image descriptor matching
چکیده انگلیسی
To overcome the high computing cost associated with high-dimensional digital image descriptor matching, this paper presents a massively parallel approximate nearest neighbor search (ANNS) on K-dimensional tree (KD-tree) on the modern massively parallel architectures (MPA). The proposed algorithm is of comparable quality to traditional sequential counterpart on central processing unit (CPU). However, it achieves a high speedup factor of 121 when applied to high-dimensional real-world image descriptor datasets. The algorithm is also studied for factors that impact its performance to obtain the optimal runtime configurations for various datasets. The performance of the proposed parallel ANNS algorithm is also verified on typical 3D image matching scenarios. With the classical local image descriptor signature of histograms of orientations (SHOT), the parallel image descriptor matching can achieve speedup of up to 128. Our implementation will potentially benefit realtime image descriptor matching in high dimensions.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Visual Communication and Image Representation - Volume 44, April 2017, Pages 106-115
نویسندگان
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