An Empirical Study on Large-Scale Content-Based Image Retrieval
One key challenge in content-based image retrieval (CBIR) is to develop a fast solution for indexing high-dimensional image contents, which is crucial to building large-scale CBIR systems. In this paper, we propose a scalable content-based image retrieval scheme using locality-sensitive hashing (LSH...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2007
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/2386 https://ink.library.smu.edu.sg/context/sis_research/article/3386/viewcontent/An_empirical_study_on_large_scale_content_based_image_retrieval_av.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | One key challenge in content-based image retrieval (CBIR) is to develop a fast solution for indexing high-dimensional image contents, which is crucial to building large-scale CBIR systems. In this paper, we propose a scalable content-based image retrieval scheme using locality-sensitive hashing (LSH), and conduct extensive evaluations on a large image testbed of a half million images. To the best of our knowledge, there is less comprehensive study on large-scale CBIR evaluation with a half million images. Our empirical results show that our proposed solution is able to scale for hundreds of thousands of images, which is promising for building Web-scale CBIR systems. |
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