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Proceedings chapter
English

Fast content identification in high-dimensional feature spaces using Sparse Ternary Codes

Presented at Abu Dhabi (United Arab Emirates), 4-7 December 2016
PublisherIEEE
Publication date2016
Abstract

We consider the problem of fast content identification in high-dimensional feature spaces where a sub-linear search complexity is required. By formulating the problem as sparse approximation of projected coefficients, a closed-form solution can be found which we approximate as a ternary representation. Hence, as opposed to dense binary codes, a framework of Sparse Ternary Codes (STC) is proposed resulting in sparse, but robust representation and sub-linear complexity of search. The proposed method is compared with the Locality Sensitive Hashing (LSH) and the memory vectors on several large-scale synthetic and public image databases, showing its superiority.

Keywords
  • Complexity theory
  • Databases
  • Search problems
  • Binary codes
  • Feature extraction
  • Table lookup
  • Signal processing algorithms
Citation (ISO format)
FERDOWSI, Sohrab et al. Fast content identification in high-dimensional feature spaces using Sparse Ternary Codes. In: IEEE International Workshop on Information Forensics and Security, WIFS 2016. Abu Dhabi (United Arab Emirates). [s.l.] : IEEE, 2016. p. 1–6. doi: 10.1109/WIFS.2016.7823919
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Proceedings chapter (Published version)
accessLevelRestricted
Identifiers
ISBN978-1-5090-1138-4
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