Article

Automatic image orientation determination with natural image statistics

Author:

Siwei LyuAuthors Info & Claims

MULTIMEDIA '05: Proceedings of the 13th annual ACM international conference on Multimedia

Pages 491 - 494

https://doi.org/10.1145/1101149.1101259

Published: 06 November 2005 Publication History

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Abstract

In this paper, we propose a new method for automatically determining image orientations. This method is based on a set of natural image statistics collected from a multi-scale multi-orientation image decomposition (e.g., wavelets). From these statistics, a two-stage hierarchal classification with multiple binary SVM classifiers is employed to determine image orientation. The proposed method is evaluated and compared to existing methods with experiments performed on 18040 natural images, where it showed promising performance.

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Cited By

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Bai RGuo X(2024)Abstract painting image orientation recognition based on eye movement and multitask learningJournal of Electronic Imaging10.1117/1.JEI.33.2.02301833:02Online publication date: 1-Mar-2024
https://doi.org/10.1117/1.JEI.33.2.023018
Ruyi B(2023)A general image orientation detection method by feature fusionThe Visual Computer10.1007/s00371-023-02782-540:1(287-302)Online publication date: 28-Jan-2023
https://doi.org/10.1007/s00371-023-02782-5
Amjoud AAmrouch M(2022)Transfer Learning for Automatic Image Orientation Detection Using Deep Learning and Logistic RegressionIEEE Access10.1109/ACCESS.2022.322545510(128543-128553)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3225455
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Index Terms

Automatic image orientation determination with natural image statistics
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

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Published In

MULTIMEDIA '05: Proceedings of the 13th annual ACM international conference on Multimedia

November 2005

1110 pages

ISBN:1595930442

DOI:10.1145/1101149

General Chairs:
Hongjiang Zhang
Microsoft Research Asia, China
,
Tat-Seng Chua
National University of Singapore, Singapore
,
Program Chairs:
Ralf Steinmetz
Technische Universitat Darmstadt, Germany
,
Mohan Kankanhalli
National University of Singapore, Singapore
,
Lynn Wilcox
FXPAL

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 06 November 2005

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MM05: 2005 13th Annual ACM International Conference on Multimedia

November 6 - 11, 2005

Hilton, Singapore

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MULTIMEDIA '05 Paper Acceptance Rate 49 of 312 submissions, 16%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

View all

Bai RGuo X(2024)Abstract painting image orientation recognition based on eye movement and multitask learningJournal of Electronic Imaging10.1117/1.JEI.33.2.02301833:02Online publication date: 1-Mar-2024
https://doi.org/10.1117/1.JEI.33.2.023018
Ruyi B(2023)A general image orientation detection method by feature fusionThe Visual Computer10.1007/s00371-023-02782-540:1(287-302)Online publication date: 28-Jan-2023
https://doi.org/10.1007/s00371-023-02782-5
Amjoud AAmrouch M(2022)Transfer Learning for Automatic Image Orientation Detection Using Deep Learning and Logistic RegressionIEEE Access10.1109/ACCESS.2022.322545510(128543-128553)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3225455
Swami KDeshpande PKhandelwal GVijayvargiya A(2017)Why my photos look sideways or upside down? Detecting canonical orientation of images using convolutional neural networks2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)10.1109/ICMEW.2017.8026216(495-500)Online publication date: Jul-2017
https://doi.org/10.1109/ICMEW.2017.8026216
Liu JDong WZhang XJiang Z(2017)Orientation judgment for abstract paintingsMultimedia Tools and Applications10.1007/s11042-015-3104-576:1(1017-1036)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1007/s11042-015-3104-5
Ciocca GCusano CSchettini R(2015)Image orientation detection using LBP-based features and logistic regressionMultimedia Tools and Applications10.1007/s11042-013-1766-474:9(3013-3034)Online publication date: 1-May-2015
https://dl.acm.org/doi/10.1007/s11042-013-1766-4
Borawski MFrejlichowski D(2012)An algorithm for the automatic estimation of image orientationProceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition10.1007/978-3-642-31537-4_26(336-344)Online publication date: 13-Jul-2012
https://dl.acm.org/doi/10.1007/978-3-642-31537-4_26
Kim JChung WCho H(2010)A new image-based CAPTCHA using the orientation of the polygonally cropped sub-imagesThe Visual Computer10.1007/s00371-010-0469-326:6-8(1135-1143)Online publication date: 14-Apr-2010
https://doi.org/10.1007/s00371-010-0469-3
Gossweiler RKamvar MBaluja SQuemada JLeón GMaarek YNejdl W(2009)What's up CAPTCHA?Proceedings of the 18th international conference on World wide web10.1145/1526709.1526822(841-850)Online publication date: 20-Apr-2009
https://dl.acm.org/doi/10.1145/1526709.1526822
Baluja S(2007)Automated image-orientation detection: a scalable boosting approachPattern Analysis & Applications10.1007/s10044-006-0059-110:3(247-263)Online publication date: 25-Jul-2007
https://dl.acm.org/doi/10.1007/s10044-006-0059-1
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Noise level estimation using locality preserving natural image statistics

Image quality assessment measure based on natural image statistics in the tetrolet domain

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