FhVLAD: Fine-grained quantization and encoding high-order descriptor statistics for scalable image retrieval

We are interested in the encoding of local descriptors of an image ( e.g. SIFT) to design a compact representation vector and thereby address scalable image retrieval. We revisit the implicit design choices in the popular vector of locally aggregated descriptors (VLAD), which aggregates the residual...

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Vydáno v:	Multimedia tools and applications Ročník 80; číslo 28-29; s. 35495 - 35520
Hlavní autoři:	Bhowmick, Alexy, Saharia, Sarat, Hazarika, Shyamanta M.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.11.2021 Springer Nature B.V
Témata:	1166: Advances of machine learning in data analytics and visual information processing Computer Communication Networks Computer Science Data Structures and Information Theory Image management Image retrieval Measurement Multimedia Information Systems Special Purpose and Application-Based Systems Statistics VLAD Fine-grained Residual High-order Image retrieval
ISSN:	1380-7501, 1573-7721
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Abstract	We are interested in the encoding of local descriptors of an image ( e.g. SIFT) to design a compact representation vector and thereby address scalable image retrieval. We revisit the implicit design choices in the popular vector of locally aggregated descriptors (VLAD), which aggregates the residuals of descriptors to the codewords. VLAD’s use of a coarse codebook and first-order descriptor statistics in residual computation results in less discriminative residuals. To address this problem, we propose a division of codebook feature space using a novel fine-grained quantization strategy. After quantization, we embed the resulting residuals with high-order statistics of descriptor distribution. Experiments on three challenging image retrieval datasets (INRIA Holidays, UKBench, Oxford 5k) confirm the improved discriminative power of our novel encoding method called FhVLAD. We observe superior accuracy to baseline and competitive performance to state-of-the-art techniques with a limited increase in dimension.
AbstractList	We are interested in the encoding of local descriptors of an image (e.g. SIFT) to design a compact representation vector and thereby address scalable image retrieval. We revisit the implicit design choices in the popular vector of locally aggregated descriptors (VLAD), which aggregates the residuals of descriptors to the codewords. VLAD’s use of a coarse codebook and first-order descriptor statistics in residual computation results in less discriminative residuals. To address this problem, we propose a division of codebook feature space using a novel fine-grained quantization strategy. After quantization, we embed the resulting residuals with high-order statistics of descriptor distribution. Experiments on three challenging image retrieval datasets (INRIA Holidays, UKBench, Oxford 5k) confirm the improved discriminative power of our novel encoding method called FhVLAD. We observe superior accuracy to baseline and competitive performance to state-of-the-art techniques with a limited increase in dimension. We are interested in the encoding of local descriptors of an image ( e.g. SIFT) to design a compact representation vector and thereby address scalable image retrieval. We revisit the implicit design choices in the popular vector of locally aggregated descriptors (VLAD), which aggregates the residuals of descriptors to the codewords. VLAD’s use of a coarse codebook and first-order descriptor statistics in residual computation results in less discriminative residuals. To address this problem, we propose a division of codebook feature space using a novel fine-grained quantization strategy. After quantization, we embed the resulting residuals with high-order statistics of descriptor distribution. Experiments on three challenging image retrieval datasets (INRIA Holidays, UKBench, Oxford 5k) confirm the improved discriminative power of our novel encoding method called FhVLAD. We observe superior accuracy to baseline and competitive performance to state-of-the-art techniques with a limited increase in dimension.
Author	Hazarika, Shyamanta M. Saharia, Sarat Bhowmick, Alexy
Author_xml	– sequence: 1 givenname: Alexy orcidid: 0000-0003-2334-5856 surname: Bhowmick fullname: Bhowmick, Alexy email: alexy.bhowmick@gmail.com organization: Department of Computer Science and Engineering, Assam Don Bosco University, Department of Computer Science and Engineering, Tezpur University – sequence: 2 givenname: Sarat surname: Saharia fullname: Saharia, Sarat organization: Department of Computer Science and Engineering, Tezpur University – sequence: 3 givenname: Shyamanta M. surname: Hazarika fullname: Hazarika, Shyamanta M. organization: Biomimetic Robotics and Artificial Intelligence Lab, Department of Mechanical Engineering, Indian Institute of Technology
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Title	FhVLAD: Fine-grained quantization and encoding high-order descriptor statistics for scalable image retrieval
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