Enhanced VLAD

• Benchang Wei,
• Tao Guan,
• Yawei Luo,
• Liya Duan,
• Junqing Yu,

Vol. 10, No. 7, July 30, 2016
10.3837/tiis.2016.07.022, Download Paper (Free):

• hierarchical-codebook
• enhanced VLAD
• projected residual vector quantization
• multiple inverted files

Abstract

Recently, Vector of Locally Aggregated Descriptors (VLAD) has been proposed to index image by compact representations, which encodes powerful local descriptors and makes significant improvement on search performance with less memory compared against the state of art. However, its performance relies heavily on the size of the codebook which is used to generate VLAD representation. It indicates better accuracy needs higher dimensional representation. Thus, more memory overhead is needed. In this paper, we enhance VLAD image representation by using two level hierarchical-codebooks. It can provide more accurate search performance while keeping the VLAD size unchanged. In addition, hierarchical-codebooks are used to construct multiple inverted files for more accurate non-exhaustive search. Experimental results show that our method can make significant improvement on both VLAD image representation and non-exhaustive search.

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