The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 Binary Feature와 Bag-of-Visual Word Framework 기반의 모바일 시각적 검색을 위한 기능 설계 방법을 제안한다. 모바일 시각 검색에서는 시점 변경으로 인한 검출 오류와 양자화 오류가 불가피하며 성능 저하를 초래합니다. 시각적 검색에 대한 일반적인 접근 방식은 참조 이미지의 단일 보기에서 특징을 추출하지만 이러한 특징은 감지 및 양자화 오류를 관리하기에는 충분하지 않습니다. 본 논문에서는 다중 시점 합성 영상에서 특징을 추출합니다. 이러한 기능은 다양한 관점 변화에 대해 강력한 인식을 가능하게 하는 새로운 신뢰성 척도에 따라 선택됩니다. 우리는 기능 선택을 최대 적용 범위 문제로 간주합니다. 즉, 특정 제약 조건 하에서 목적 함수를 최대화하는 유한한 특성 집합을 찾습니다. 이 문제는 NP-난해하여 계산적으로 실행 불가능하므로 그리디 알고리즘을 기반으로 한 대략적인 솔루션을 탐색합니다. 이를 위해 우리는 시각적 검색 방법의 일치 조건과 일치하도록 설계된 새로운 제약 기능을 제안합니다. 실험 결과 제안된 방법은 데이터베이스 크기를 늘리거나 검색 절차를 변경하지 않고도 검색 정확도를 12.7% 향상시키는 것으로 나타났습니다. 즉, 제안된 방법은 데이터베이스 크기, 계산 비용 및 메모리 요구 사항에 부정적인 영향을 주지 않으면서 보다 정확한 검색이 가능합니다.
Kohei MATSUZAKI
KDDI Research, Inc.
Kazuyuki TASAKA
KDDI Research, Inc.
Hiromasa YANAGIHARA
KDDI Research, Inc.
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Kohei MATSUZAKI, Kazuyuki TASAKA, Hiromasa YANAGIHARA, "Local Feature Reliability Measure Consistent with Match Conditions for Mobile Visual Search" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 12, pp. 3170-3180, December 2018, doi: 10.1587/transinf.2018EDP7107.
Abstract: We propose a feature design method for a mobile visual search based on binary features and a bag-of-visual words framework. In mobile visual search, detection error and quantization error are unavoidable due to viewpoint changes and cause performance degradation. Typical approaches to visual search extract features from a single view of reference images, though such features are insufficient to manage detection and quantization errors. In this paper, we extract features from multiview synthetic images. These features are selected according to our novel reliability measure which enables robust recognition against various viewpoint changes. We regard feature selection as a maximum coverage problem. That is, we find a finite set of features maximizing an objective function under certain constraints. As this problem is NP-hard and thus computationally infeasible, we explore approximate solutions based on a greedy algorithm. For this purpose, we propose novel constraint functions which are designed to be consistent with the match conditions in the visual search method. Experiments show that the proposed method improves retrieval accuracy by 12.7 percentage points without increasing the database size or changing the search procedure. In other words, the proposed method enables more accurate search without adversely affecting the database size, computational cost, and memory requirement.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7107/_p
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@ARTICLE{e101-d_12_3170,
author={Kohei MATSUZAKI, Kazuyuki TASAKA, Hiromasa YANAGIHARA, },
journal={IEICE TRANSACTIONS on Information},
title={Local Feature Reliability Measure Consistent with Match Conditions for Mobile Visual Search},
year={2018},
volume={E101-D},
number={12},
pages={3170-3180},
abstract={We propose a feature design method for a mobile visual search based on binary features and a bag-of-visual words framework. In mobile visual search, detection error and quantization error are unavoidable due to viewpoint changes and cause performance degradation. Typical approaches to visual search extract features from a single view of reference images, though such features are insufficient to manage detection and quantization errors. In this paper, we extract features from multiview synthetic images. These features are selected according to our novel reliability measure which enables robust recognition against various viewpoint changes. We regard feature selection as a maximum coverage problem. That is, we find a finite set of features maximizing an objective function under certain constraints. As this problem is NP-hard and thus computationally infeasible, we explore approximate solutions based on a greedy algorithm. For this purpose, we propose novel constraint functions which are designed to be consistent with the match conditions in the visual search method. Experiments show that the proposed method improves retrieval accuracy by 12.7 percentage points without increasing the database size or changing the search procedure. In other words, the proposed method enables more accurate search without adversely affecting the database size, computational cost, and memory requirement.},
keywords={},
doi={10.1587/transinf.2018EDP7107},
ISSN={1745-1361},
month={December},}
부
TY - JOUR
TI - Local Feature Reliability Measure Consistent with Match Conditions for Mobile Visual Search
T2 - IEICE TRANSACTIONS on Information
SP - 3170
EP - 3180
AU - Kohei MATSUZAKI
AU - Kazuyuki TASAKA
AU - Hiromasa YANAGIHARA
PY - 2018
DO - 10.1587/transinf.2018EDP7107
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2018
AB - We propose a feature design method for a mobile visual search based on binary features and a bag-of-visual words framework. In mobile visual search, detection error and quantization error are unavoidable due to viewpoint changes and cause performance degradation. Typical approaches to visual search extract features from a single view of reference images, though such features are insufficient to manage detection and quantization errors. In this paper, we extract features from multiview synthetic images. These features are selected according to our novel reliability measure which enables robust recognition against various viewpoint changes. We regard feature selection as a maximum coverage problem. That is, we find a finite set of features maximizing an objective function under certain constraints. As this problem is NP-hard and thus computationally infeasible, we explore approximate solutions based on a greedy algorithm. For this purpose, we propose novel constraint functions which are designed to be consistent with the match conditions in the visual search method. Experiments show that the proposed method improves retrieval accuracy by 12.7 percentage points without increasing the database size or changing the search procedure. In other words, the proposed method enables more accurate search without adversely affecting the database size, computational cost, and memory requirement.
ER -