High Performance Online Image Search with GPUs on Large Image Databases

Ali Cevahir and Junji Torii
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Ali Cevahir: Rakuten Institute of Technology, Rakuten Inc., Tokyo, Japan
Junji Torii: Rakuten Institute of Technology, Rakuten Inc., Tokyo, Japan

International Journal of Multimedia Data Engineering and Management (IJMDEM), 2013, vol. 4, issue 3, 24-41

Abstract: The authors propose an online image search engine based on local image keypoint matching with GPU support. State-of-the-art models are based on bag-of-visual-words, which is an analogy of textual search for visual search. In this work, thanks to the vector computation power of the GPU, the authors utilize real values of keypoint descriptors and realize real-time search at keypoint level. By keeping the identities of each keypoint, closest keypoints are accurately retrieved. Image search has different characteristics than textual search. The authors implement one-to-one keypoint matching, which is more natural for images. The authors utilize GPUs for every basic step. To demonstrate practicality of GPU-extended image search, the authors also present a simple bag-of-visual-words search technique with full-text search engines. The authors explain how to implement one-to-one keypoint matching with text search engine. Proposed methods lead to drastic performance and precision improvement, which is demonstrated on datasets of different sizes.

Date: 2013
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