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학술지 Quadra-Embedding: Binary Code Embedding with Low Quantization Error
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저자
이영운, 허재필, 윤성의
발행일
201408
출처
Computer Vision and Image Understanding, v.125, pp.214-222
ISSN
1077-3142
출판사
Elsevier
DOI
https://dx.doi.org/10.1016/j.cviu.2014.04.007
협약과제
14MS1700, 학습자 참여형 인터랙션 3D 입체 가상체험 학습 콘텐츠 기술 개발, 이준석
초록
Thanks to compact data representations and fast similarity computation, many binary code embedding techniques have been proposed for large-scale similarity search used in many computer vision applications including image retrieval. Most prior techniques have centered around optimizing a set of projections for accurate embedding. In spite of active research efforts, existing solutions suffer from diminishing marginal efficiency and high quantization errors as more code bits are used. To reduce both quantization error and diminishing efficiency we propose a novel binary code embedding scheme, Quadra-Embedding, that assigns two bits for each projection to define four quantization regions, and a binary code distance function tailored to our method. Our method is directly applicable to most binary code embedding methods. Our scheme combined with four state-of-the-art embedding methods has been evaluated and achieves meaningful accuracy improvement in most experimental configurations. © 2014 Elsevier Inc. All rights reserved.
키워드
Binary code embedding, Hashing, Large-scale image retrieval, Quantization
KSP 제안 키워드
Binary codes, Code distance, Computer Vision(CV), Data representation, Embedding Technique, Marginal efficiency, Quantization error, Similarity computation, Similarity search, accuracy improvement, computer vision applications