skip to main content
10.1145/3167132.3167374acmconferencesArticle/Chapter ViewAbstractPublication PagessacConference Proceedingsconference-collections
poster

BoSS: image retrieval using bag-of-superpixels signatures

Published: 09 April 2018 Publication History

Abstract

Techniques of bags-of-visual-words based on signature have been employed in image retrieval and analysis, with the benefit of dismissing expensive clustering processes. However, the limitations of such techniques are the requirement of multiple parameters, which may be unintuitive and in most cases depends on the application domain. In this paper, we overcome these limitations by proposing Bag-of-Superpixel Signatures (BoSS), which extracts visual signatures using local features from superpixels. Moreover, our proposal also employs a fractal analysis to extract intrinsic information about the domain application and also to diminish the amount of parameters needed. The results demonstrated that BoSS achieved an improvement up to 31.2% in image retrieval precision during experimental evaluations over five distinct datasets. We conclude that BoSS introduces an intuitive, self-contained, scalable and effective approach for image retrieval using bags-of-visual words.

References

[1]
R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk. 2012. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE TPAMI 34, 11 (2012), 2274--2282.
[2]
M. Aly, P. Welinder, M. E. Munich, and P. Perona. 2009. Towards automated large scale discovery of image families. In IEEE CVPR. 9--16.
[3]
D. Arthur and S. Vassilvitskii. 2007. K-means++: The Advantages of Careful Seeding. In ACM-SIAM SODA. 1027--1035.
[4]
D. Barbara and P. Chen. 2009. Fractal mining-self similarity-based clustering and its applications. In DMKD Handbook. Springer, 573--589.
[5]
M. V. N. Bedo, G. Blanco, W.D. Oliveira, M. T. Cazzolato, A.F. Costa, J.F. Rodrigues Jr., A. J. M. Traina, and C. Traina Jr. 2015. Techniques for Effective and Efficient Fire Detection from Social Media Images. In ICEIS. 34--45.
[6]
C. Caetano, S. Avila, S. Guimarães, and A. de A. Araújo. 2014. Representing Local Binary Descriptors with BossaNova for Visual Recognition. In ACM SAC. 49--54.
[7]
P. Devineni, D. Koutra, M. Faloutsos, and C. Faloutsos. 2015. If walls could talk: Patterns and anomalies in Facebook wallposts. In IEEE/ACM ASONAM. 367--374.
[8]
J. M. dos Santos, E. S. de Moura, A. S. da Silva, J. M. B. Cavalcanti, R. da S. Torres, and M. L. A. Vidal. 2015. A signature-based bag of visual words method for image indexing and search. Pattern Recognition Letters 65 (2015), 1--7.
[9]
J. M. dos Santos, E. S. de Moura, A. S. da Silva, and R. da S. Torres. 2017. Color and texture applied to a signature-based bag of visual words method for image retrieval. MTA 76, 15 (2017), 16855--16872.
[10]
J. Feng, X. Liu, Y. Dong, L. Liang, and J. Pu. 2017. Structural difference histogram representation for texture image classification. IET Img Proc. 11, 2 (2017), 118--125.
[11]
A. C. Fraideinberze, J. F. Rodrigues, and R. L. F. Cordeiro. 2016. Effective and Unsupervised Fractal-Based Feature Selection for Very Large Datasets: Removing Linear and Non-linear Attribute Correlations. In IEEE ICDMW. 615--622.
[12]
H. Jegou, M. Douze, and C. Schmid. 2008. Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search. In ECCV. 304--317.
[13]
M. Juneja, A. Vedaldi, C. V. Jawahar, and A. Zisserman. 2013. Blocks that shout: Distinctive parts for scene classification. In IEEE CVPR. 923--930.
[14]
S. Lazebnik, C. Schmid, and J. Ponce. 2005. A Sparse Texture Representation Using Local Affine Regions. IEEE TPAMI 27, 8 (2005), 1265--1278.
[15]
Z. Li,X. M. Wu, andS. F. Chang. 2012. Segmentation using superpixels: A bipartite graph partitioning approach. In CVPR. 789--796.
[16]
M. Schroeder. 2012. Fractals, chaos, power laws: Minutes from an infinite paradise. Dover Publications, Incorporated.
[17]
D. Sculley. 2010. Web-scale K-means Clustering. In WWW. 1177--1178.
[18]
J. Sivic and A. Zisserman. 2003. Video Google: A Text Retrieval Approach to Object Matching in Videos. In ICCV. 1470--1477.
[19]
C. Traina Jr., A. J. M. Traina, and C. Faloutsos. 2010. Fast Feature Selection using Fractal Dimension - Ten Years Later. JIDM 1, 1 (2010), 17--20.
[20]
J. Z. Wang, J. Li, and G. Wiederhold. 2001. SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries. IEEE TPAMI 23, 9 (2001), 947--963.
[21]
K. Wang, L. Yang, G. Yang, X. Luo, K. Su, and Y. Yin. 2017. Finger Vein Image Retrieval via Coding Scale-varied Superpixel Feature. In ICMR. 375--382.
[22]
J. Yang, Y. G. Jiang, A. G. Hauptmann, and C. W. Ngo. 2007. Evaluating bag-of-visual-words representations in scene classification. In ACM SIGMM MIR. 197--206.
[23]
J. Zeng, J. Duan, W. Cao, and C. Wu. 2012. Topics modeling based on selective Zipf distribution. Expert Syst. Appl. 39, 7 (2012), 6541--6546.
[24]
C. Zhang, Z. Ni, L. Ni, and N. Tang. 2016. Feature selection method based on multi-fractal dimension and harmony search algorithm and its application. IJSS 47, 14 (2016), 3476--3486.
[25]
R. Zhang, M. Zhou, X. Gong, X. He, W. Qian, S. Qin, and A. Zhou. 2015. Detecting anomaly in data streams by fractal model. WWW 18, 5 (01 Sep 2015), 1419--1441.

Cited By

View all
  • (2023)BoCS: Image Retrieval Using Explicable Methods2023 36th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)10.1109/SIBGRAPI59091.2023.10347171(235-240)Online publication date: 6-Nov-2023
  • (2018)ICARUS: Retrieving Skin Ulcer Images through Bag-of-Signatures2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS)10.1109/CBMS.2018.00022(82-87)Online publication date: Jun-2018

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing
April 2018
2327 pages
ISBN:9781450351911
DOI:10.1145/3167132
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 April 2018

Check for updates

Author Tags

  1. bag-of-visual-words
  2. image retrieval
  3. visual signature

Qualifiers

  • Poster

Conference

SAC 2018
Sponsor:
SAC 2018: Symposium on Applied Computing
April 9 - 13, 2018
Pau, France

Acceptance Rates

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

Upcoming Conference

SAC '25
The 40th ACM/SIGAPP Symposium on Applied Computing
March 31 - April 4, 2025
Catania , Italy

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)1
  • Downloads (Last 6 weeks)0
Reflects downloads up to 17 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2023)BoCS: Image Retrieval Using Explicable Methods2023 36th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)10.1109/SIBGRAPI59091.2023.10347171(235-240)Online publication date: 6-Nov-2023
  • (2018)ICARUS: Retrieving Skin Ulcer Images through Bag-of-Signatures2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS)10.1109/CBMS.2018.00022(82-87)Online publication date: Jun-2018

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media