Article

Use of affine invariants in locally likely arrangement hashing for camera-based document image retrieval

Authors:

Tomohiro Nakai,

Koichi Kise,

Masakazu IwamuraAuthors Info & Claims

DAS'06: Proceedings of the 7th international conference on Document Analysis Systems

Pages 541 - 552

https://doi.org/10.1007/11669487_48

Published: 13 February 2006 Publication History

Abstract

Camera-based document image retrieval is a task of searching document images from the database based on query images captured using digital cameras. For this task, it is required to solve the problem of “perspective distortion” of images,as well as to establish a way of matching document images efficiently. To solve these problems we have proposed a method called Locally Likely Arrangement Hashing (LLAH) which is characterized by both the use of a perspective invariant to cope with the distortion and the efficiency: LLAH only requires O(N) time where N is the number of feature points that describe the query image. In this paper, we introduce into LLAH an affine invariant instead of the perspective invariant so as to improve its adjustability. Experimental results show that the use of the affine invariant enables us to improve either the accuracy from 96.2% to 97.8%, or the retrieval time from 112 msec./query to 75 msec./query by selecting parameters of processing.

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Index Terms

Use of affine invariants in locally likely arrangement hashing for camera-based document image retrieval
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
  2. Computer graphics
    1. Image manipulation

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

DAS'06: Proceedings of the 7th international conference on Document Analysis Systems

February 2006

627 pages

ISBN:3540321403

Editors:
Horst Bunke
Institute of Computer Science and Applied Mathematics, University of Bern, Neubrückstrasse 10, Bern, Switzerland
,
A. Lawrence Spitz
Institute of Computer Science and Applied Mathematics, DocRec Ltd, 34 Strathaven Place, Atawhai, Nelson, New Zealand

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 13 February 2006

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Lomaliza JPark HMorishima SItoh YShiratori TYue YLindeman R(2018)Learning-based word segmentation for reliable text document retrieval and augmentationProceedings of the 24th ACM Symposium on Virtual Reality Software and Technology10.1145/3281505.3281585(1-2)Online publication date: 28-Nov-2018
https://dl.acm.org/doi/10.1145/3281505.3281585
Rusiñol MChazalon JDiaz-Chito K(2018)Augmented songbookMultimedia Tools and Applications10.1007/s11042-017-4991-477:11(13773-13798)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1007/s11042-017-4991-4
Narita GWatanabe YIshikawa M(2017)Dynamic Projection Mapping onto Deforming Non-Rigid Surface Using Deformable Dot Cluster MarkerIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2016.259291023:3(1235-1248)Online publication date: 1-Mar-2017
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Máttyus GFraundorfer F(2016)Aerial image sequence geolocalization with road traffic as invariant featureImage and Vision Computing10.1016/j.imavis.2016.05.01452:C(218-229)Online publication date: 1-Aug-2016
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Yang LNormand JMoreau GLau RManocha DKomura TMajumder AXu W(2014)Robust random dot markersProceedings of the 20th ACM Symposium on Virtual Reality Software and Technology10.1145/2671015.2671022(45-54)Online publication date: 11-Nov-2014
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Mazzei AEivazi SMarko YKaplan FDillenbourg PQvarfordt PHansen D(2014)3D model-based gaze estimation in natural readingProceedings of the Symposium on Eye Tracking Research and Applications10.1145/2578153.2578164(87-90)Online publication date: 26-Mar-2014
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Kunze KTanaka KIwamura MKise KMattern FSantini SCanny JLangheinrich MRekimoto J(2013)Annotate meProceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication10.1145/2494091.2494165(231-234)Online publication date: 8-Sep-2013
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