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        首頁 > 論文 > 激光與光電子學進展 > 56卷 > 14期(pp:141008--1)

        基于卷積神經網絡的SIFT特征描述子降維方法

        Convolutional Neural Network-Based Dimensionality Reduction Method for Image Feature Descriptors Extracted Using Scale-Invariant Feature Transform

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        摘要

        針對128維尺度不變特征變換(SIFT)特征描述子進行圖像局部特征點提取時匹配時間過長,以及三維重建進行特征點配準時的應用局限性,結合深度學習方法,提出一種基于卷積神經網絡的SIFT特征描述子降維方法。該方法利用卷積神經網絡強大的學習能力實現了SIFT特征描述子降維,同時保留了良好的仿射變換不變性。實驗結果表明,經過訓練的卷積神經網絡將SIFT特征描述子降至32維時,新的特征描述子在旋轉、尺度、視點以及光照等仿射變換下均具有良好的匹配效果,匹配效率比傳統SIFT特征描述子效率提高了5倍。

        Abstract

        Since local feature descriptors extracted from an image using the traditional scale-invariant feature transform (SIFT) method are 128-dimensional vectors, the matching time is too long, which limits their applicability in some cases such as feature point matching based on the three-dimensional reconstruction. To tackle this problem, a SIFT feature descriptor dimensionality reduction method based on a convolutional neural network is proposed. The powerful learning ability of the convolutional neural network is used to realize the dimensionality reduction of SIFT feature descriptors while maintaining their good affine transformation invariance. The experimental results demonstrate that the new feature descriptors obtained using the proposed method generalize well against affine transformations, such as rotation, scale, viewpoint, and illumination, after reducing their dimensionality to 32. Furthermore, the matching speed of the feature descriptors obtained using the proposed method is nearly five times faster than that of the SIFT feature descriptors.

        Newport宣傳-MKS新實驗室計劃
        補充資料

        DOI:10.3788/LOP56.141008

        所屬欄目:圖像處理

        基金項目:國家自然科學基金、國家973計劃;

        收稿日期:2019-01-14

        修改稿日期:2019-02-21

        網絡出版日期:2019-07-01

        作者單位    點擊查看

        周宏浩:中國科學技術大學環境科學與光電技術學院, 安徽 合肥 230031中國科學院通用光學與表征技術重點實驗室, 安徽 合肥 230031
        易維寧:中國科學院通用光學與表征技術重點實驗室, 安徽 合肥 230031
        杜麗麗:中國科學院通用光學與表征技術重點實驗室, 安徽 合肥 230031
        喬延利:中國科學技術大學環境科學與光電技術學院, 安徽 合肥 230031中國科學院通用光學與表征技術重點實驗室, 安徽 合肥 230031

        聯系人作者:杜麗麗, 喬延利(ylqiao@aiofm.ac.cn, lilydu@aiofm.ac.cn)

        備注:國家自然科學基金、國家973計劃;

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        引用該論文

        Honghao Zhou, Weining Yi, Lili Du, Yanli Qiao. Convolutional Neural Network-Based Dimensionality Reduction Method for Image Feature Descriptors Extracted Using Scale-Invariant Feature Transform[J]. Laser & Optoelectronics Progress, 2019, 56(14): 141008

        周宏浩, 易維寧, 杜麗麗, 喬延利. 基于卷積神經網絡的SIFT特征描述子降維方法[J]. 激光與光電子學進展, 2019, 56(14): 141008

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