著者
Takaharu KATO Ikuko SHIMIZU Tomas PAJDLA
出版者
The Institute of Electronics, Information and Communication Engineers
雑誌
IEICE Transactions on Information and Systems (ISSN:09168532)
巻号頁・発行日
vol.E105.D, no.9, pp.1590-1599, 2022-09-01 (Released:2022-09-01)
参考文献数
39

Selecting visually overlapping image pairs without any prior information is an essential task of large-scale structure from motion (SfM) pipelines. To address this problem, many state-of-the-art image retrieval systems adopt the idea of bag of visual words (BoVW) for computing image-pair similarity. In this paper, we present a method for improving the image pair selection using BoVW. Our method combines a conventional vector-based approach and a set-based approach. For the set similarity, we introduce a modified version of the Simpson (m-Simpson) coefficient. We show the advantage of this measure over three typical set similarity measures and demonstrate that the combination of vector similarity and the m-Simpson coefficient effectively reduces false positives and increases accuracy. To discuss the choice of vocabulary construction, we prepared both a sampled vocabulary on an evaluation dataset and a basic pre-trained vocabulary on a training dataset. In addition, we tested our method on vocabularies of different sizes. Our experimental results show that the proposed method dramatically improves precision scores especially on the sampled vocabulary and performs better than the state-of-the-art methods that use pre-trained vocabularies. We further introduce a method to determine the k value of top-k relevant searches for each image and show that it obtains higher precision at the same recall.

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Improving image pair selection for large scale Structure from Motion by introducing modified Simpson coefficient Takaharu Kato, Ikuko Shimuzu,Tomas Pajdla tl;dr: improving BoW-based co-visible image retrieval for SfM (does pair contain same scene?) https://t.co/uVSVHhpzRC https://t.co/FlHrSCC419

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