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• W2000284985 abstract "Indexing images and videos using binary hash bits has shown promising results for fast similarity search. Existing datadriven hashing methods learn compact hash codes from the data, but usually with the cost of generating unbalanced hash buckets, thus affecting the search efficiency. We propose a novel data-driven hashing method called forest hashing, which utilizes multiple tree structures to perform data hashing. By leveraging the index structure of trees, we can significantly improve the hashing efficacy by generating balanced hash buckets. Moreover, forest hashing naturally supports scalable coding where more trees can improve the coding quality with a longer code. Last but not the least, our forest hashing can be easily extended for semantic search by integrating semi-supervised label information. Experiments on two benchmark datasets show favorable results compared with the state-of-the-art hashing methods." @default.
• W2000284985 created "2016-06-24" @default.
• W2000284985 creator A5003400275 @default.
• W2000284985 creator A5085245110 @default.
• W2000284985 date "2014-07-01" @default.
• W2000284985 modified "2023-09-26" @default.
• W2000284985 title "Scalable forest hashing for fast similarity search" @default.
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• W2000284985 doi "https://doi.org/10.1109/icme.2014.6890219" @default.
• W2000284985 hasPublicationYear "2014" @default.
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