More Web Proxy on the site http://driver.im/

research-article

Exploiting photographic style for category-level image classification by generalizing the spatial pyramid

Author:

Jan C. van GemertAuthors Info & Claims

ICMR '11: Proceedings of the 1st ACM International Conference on Multimedia Retrieval

Article No.: 14, Pages 1 - 8

https://doi.org/10.1145/1991996.1992010

Published: 18 April 2011 Publication History

Abstract

This paper investigates the use of photographic style for category-level image classification. Specifically, we exploit the assumption that images within a category share a similar style defined by attributes such as colorfulness, lighting, depth of field, viewpoint and saliency. For these style attributes we create correspondences across images by a generalized spatial pyramid matching scheme. Where the spatial pyramid groups features spatially, we allow more general feature grouping and in this paper we focus on grouping images on photographic style. We evaluate our approach in an object classification task and investigate style differences between professional and amateur photographs. We show that a generalized pyramid with style-based attributes improves performance on the professional Corel and amateur Pascal VOC 2009 image datasets.

References

[1]

R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk. Frequency-tuned Salient Region Detection. In CVPR, 2009.

[2]

S. Banerjee and B. Evans. In-camera automation of photographic composition rules. Trans. Image Processing, 16(7), 2007.

Digital Library

[3]

Y.-Y. Chang and H.-T. Chen. Finding good composition in panoramic scenes. In ICCV, 2009.

[4]

R. Datta, D. Joshi, J. Li, and J. Z. Wang. Studying aesthetics in photographic images using a computational approach. In ECCV, 2006.

Digital Library

[5]

M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2009 Results, 2009.

[6]

A. Farhadi, I. Endres, D. Hoiem, and D. A. Forsyth. Describing objects by their attributes. In CVPR, 2009.

[7]

V. Ferrari and A. Zisserman. Learning visual attributes. In NIPS, 2007.

Digital Library

[8]

K. Grauman and T. Darrell. The pyramid match kernel: Discriminative classification with sets of image features. In ICCV, 2005.

Digital Library

[9]

Y. Ke, X. Tang, and F. Jing. The design of high-level features for photo quality assessment. In CVPR, 2006.

Digital Library

[10]

F. S. Khan, J. van de Weijer, and M. Vanrell. Top-down color attention for object recognition. In ICCV, 2009.

[11]

B. P. Krages. Photography: the art of composition. Allworth Press, 2005.

[12]

C. H. Lampert, H. Nickisch, and S. Harmeling. Learning to detect unseen object classes by between-class attribute transfer. In CVPR, 2009.

[13]

S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In CVPR, 2006.

Digital Library

[14]

A. Levin. Blind motion deblurring using image statistics. In NIPS, 2006.

[15]

L. Liu, R. Chen, L. Wolf, and D. Cohen-Or. Optimizing photo composition. Proceedings of Eurographics, 29(2), 2010.

[16]

Y. Luo and X. Tang. Photo and video quality evaluation: Focusing on the subject. In ECCV, 2008.

Digital Library

[17]

K. Mikolajczyk and C. Schmid. Scale & affine invariant interest point detectors. Int. J. Comput. Vision, 60(1):63--86, 2004.

Digital Library

[18]

A. K. Moorthy, P. Obrador, and N. Oliver. Towards computational models of the visual aesthetic appeal of consumer videos. In ECCV, 2010.

Digital Library

[19]

S. Savarese, J. Winn, and A. Criminisi. Discriminative object class models of appearance and shape by correlatons. In CVPR, 2006.

Digital Library

[20]

H. Tong, M. Li, H. Zhang, J. He, and C. Zhang. Classification of digital photos taken by photographers or home users. In In Proceedings of Pacific Rim Conference on Multimedia, 2004.

Digital Library

[21]

R. Valenti, N. Sebe, and T. Gevers. Image saliency by isocentric curvedness and color. In ICCV, 2009.

[22]

K. E. A. van de Sande, T. Gevers, and C. G. M. Snoek. Evaluating color descriptors for object and scene recognition. TPAMI, 32(9):1582--1596, 2010.

Digital Library

[23]

J. van de Weijer, T. Gevers, and A. Bagdanov. Boosting color saliency in image feature detection. TPAMI, pages 150--156, 2006.

Digital Library

[24]

J. C. van Gemert, C. J. Veenman, A. W. M. Smeulders, and J. M. Geusebroek. Visual word ambiguity. TPAMI, 32(7):1271--1283, 2010.

Digital Library

[25]

J. Vogel and B. Schiele. Semantic modeling of natural scenes for content-based image retrieval. Int. J. Comput. Vision, 72(2):133--157, 2007.

Digital Library

Cited By

Lengyel AStrafforello OBruintjes RGielisse Avan Gemert JOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Color equivariant convolutional networksProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667421(29831-29850)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3667421
Gu SChung FWang S(2023)Fuzzy style flat-based clusteringInformation Sciences10.1016/j.ins.2023.119321644(119321)Online publication date: Oct-2023
https://doi.org/10.1016/j.ins.2023.119321
Semih Kayhan Ovan Gemert J(2020)On Translation Invariance in CNNs: Convolutional Layers Can Exploit Absolute Spatial Location2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR42600.2020.01428(14262-14273)Online publication date: Jun-2020
https://doi.org/10.1109/CVPR42600.2020.01428
Show More Cited By

Index Terms

Exploiting photographic style for category-level image classification by generalizing the spatial pyramid
1. Information systems
  1. Information retrieval
    1. Document representation

Recommendations

Does Haze Removal Help CNN-Based Image Classification?
Computer Vision – ECCV 2018
Abstract
Hazy images are common in real scenarios and many dehazing methods have been developed to automatically remove the haze from images. Typically, the goal of image dehazing is to produce clearer images from which human vision can better identify the ...
Level influence of spatial pyramid matching in object classification
MM '11: Proceedings of the 19th ACM international conference on Multimedia

In this paper we propose to effectively consider the shape and size variations for object classification. Specifically, a novel image matching method is proposed to incorporate the image segmentation with Spatial Pyramid Matching (SPM), and test our ...
Blurred Image Recognition: A Joint Motion Deblurring and Classification Loss-Aware Approach
Artificial Neural Networks and Machine Learning – ICANN 2021
Abstract
Image motion blur can severely affect the performance of the image recognition model. Traditional methods to tackle this problem usually involve image motion deblurring to improve the image quality before its recognition. However, traditional ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

ICMR '11: Proceedings of the 1st ACM International Conference on Multimedia Retrieval

April 2011

512 pages

ISBN:9781450303361

DOI:10.1145/1991996

General Chairs:
Francesco G. B. De Natale
University of Trento, Italy
,
Alberto Del Bimbo
University of Florence, Italy
,
Program Chairs:
Alan Hanjalic
University of Amsterdam, Netherlands
,
B. S. Manjunath
University of California, Santa Barbara
,
Shin'ichi Satoh
NII, Japan

Copyright © 2011 ACM.

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]

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 April 2011

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

ICMR'11

Sponsor:

SIGMM

ICMR'11: International Conference on Multimedia Retrieval

April 18 - 20, 2011

Trento, Italy

Acceptance Rates

Overall Acceptance Rate 254 of 830 submissions, 31%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

19
Total Citations
View Citations
252
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)1

Reflects downloads up to 31 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Lengyel AStrafforello OBruintjes RGielisse Avan Gemert JOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Color equivariant convolutional networksProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667421(29831-29850)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3667421
Gu SChung FWang S(2023)Fuzzy style flat-based clusteringInformation Sciences10.1016/j.ins.2023.119321644(119321)Online publication date: Oct-2023
https://doi.org/10.1016/j.ins.2023.119321
Semih Kayhan Ovan Gemert J(2020)On Translation Invariance in CNNs: Convolutional Layers Can Exploit Absolute Spatial Location2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR42600.2020.01428(14262-14273)Online publication date: Jun-2020
https://doi.org/10.1109/CVPR42600.2020.01428
Dakhia AWang TLu H(2020)Dynamically-Passed Contextual Information Network for Saliency DetectionPattern Recognition and Computer Vision10.1007/978-3-030-60636-7_31(369-381)Online publication date: 16-Oct-2020
https://dl.acm.org/doi/10.1007/978-3-030-60636-7_31
Elfiky N(2020)A Novel Spatial Layout Representation for Object RecognitionProceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020)10.1007/978-3-030-44289-7_52(553-563)Online publication date: 24-Mar-2020
https://doi.org/10.1007/978-3-030-44289-7_52
Jiang SShao MJia CFu Y(2018)Learning Consensus Representation for Weak Style ClassificationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2017.277176640:12(2906-2919)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1109/TPAMI.2017.2771766
Karaoglu STao Rvan Gemert JGevers T(2017)Con-Text: Text Detection for Fine-Grained Object ClassificationIEEE Transactions on Image Processing10.1109/TIP.2017.270780526:8(3965-3980)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1109/TIP.2017.2707805
Everingham MEslami SGool LWilliams CWinn JZisserman A(2015)The Pascal Visual Object Classes ChallengeInternational Journal of Computer Vision10.1007/s11263-014-0733-5111:1(98-136)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1007/s11263-014-0733-5
Zhao SGao YJiang XYao HChua TSun XHua KRui YSteinmetz RHanjalic ANatsev AZhu W(2014)Exploring Principles-of-Art Features For Image Emotion RecognitionProceedings of the 22nd ACM international conference on Multimedia10.1145/2647868.2654930(47-56)Online publication date: 3-Nov-2014
https://dl.acm.org/doi/10.1145/2647868.2654930
Mensink Tvan Gemert JKankanhalli MRueger SManmatha RJose Jvan Rijsbergen K(2014)The Rijksmuseum ChallengeProceedings of International Conference on Multimedia Retrieval10.1145/2578726.2578791(451-454)Online publication date: 1-Apr-2014
https://dl.acm.org/doi/10.1145/2578726.2578791
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents