AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (2.7 MB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Automatic Collecting Representative Logo Images from the Internet

State Key Laboratory for Intelligent Technology and System, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
Show Author Information

Abstract

With the explosive growth of commercial logos, high quality logo images are needed for training logo detection or recognition systems, especially for famous logos or new commercial brands. This paper focuses on automatic collecting representative logo images from the internet without any human labeling or seed images. We propose multiple dictionary invariant sparse coding to solve this problem. This work can automatically provide prototypes, representative images, or weak labeled training images for logo detection, logo recognition, trademark infringement detection, brand protection, and ad-targeting. The experiment results show that our method increases the mean average precision for 25 types of logos to 80.07% whereas the original search engine results only have 32% representative logo images. The top images collected by our method are accurate and reliable enough for practical applications in the future.

References

[1]
J. Kleban, X. Xie, and W. Ma, Spatial pyramid mining for logo detection in natural scenes, in Proc. of International Conference on Multimedia and Expo (ICME), Hannover, Germany, 2008, pp. 1077-1080.
[2]
G. Zhu and D. Doermann, Automatic document logo detection, in Proc. of 9th International Conference on Document Analysis and Recognition (ICDAR), Curitiba, Brazil, 2007, pp. 864-868.
[3]
J. Chen, M. Leung, and Y. Gao, Noisy logo recognition using line segment Hausdorff distance, Pattern Recognition, vol. 36, no. 4, pp. 943-955, April 2003.
[4]
H. Sulehria and Y. Zhang, Vehicle logo recognition using mathematical Morphology, in Proc. of International Conference on Telecommunications and Informatics, Xi’an, Shaanxi, China, 2007, pp. 95-98.
[5]
D. Doermann, E. Rivlin, and I. Weiss, Logo recognition using geometric invariants, in Proc. of the 2nd International Conference on Document Analysis and Recognition (ICDAR), Tsukuba, Japan, 1993, pp. 894-897.
[6]
W. Yan, J. Wang, and M. Kankanhalli, Automatic video logo detection and removal, Multimedia System, vol. 10, no. 5, pp. 379-391, August 2005.
[7]
K. Meisinger, T. Troeger, M. Zeller, and A. Kaup, Automatic TV logo removal using statistical based logo detection and frequency selective inpainting, in European Signal Processing Conference, Antalya, Turkey, 2005, pp. 147-150.
[8]
X. Liu and B. Zhang, Learning complex image patterns with scale and shift invariant sparse coding, in Proc. of 18th International Conference on Image Processing (ICIP), Brussels, Belgium, 2011, pp. 1225-1228.
[9]
T. Berg and D. Forsyth, Animals on the web, in Proc. of Computer Vision and Pattern Recognition (CVPR), New York, NY, USA, 2006, pp. 1463-1470.
[10]
J. Besemer, A. Lomsadze, and M. Borodovsky, GeneMarkS: A self-training method for prediction of gene starts in microbial genomes—Implications for finding sequence motifs in regulatory regions, Nucleic Acids Research, vol. 29, no. 12, pp. 2607-2618, 2001.
[11]
B. Collins, J. Deng, K. Li, and F. Li, Toward scalable dataset construction: An active learning approach, in Proc. of European Conference on Computer Vision (ECCV), Marseille, France, 2008, pp. 86-98.
[12]
F. Li, R. Fergus, and P. Perona, Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories, Computer Vision and Image Understanding, vol. 106, no. 1, pp. 59-70, April 2007.
[13]
R. Fergus, F. Li, P. Perona, and A. Zisserman, Learning object categories from Google image search, in Proc. of 10th IEEE International Conference on Computer Vision (ICCV), Beijing, China, 2005, pp. 1816-1823.
[14]
C. Rosenberg, M. Hebert, and H. Schneiderman, Semi-supervised self-training of object detection models, in Proc. of 7th IEEE Workshop on Applications of Computer Vision, Breckenridge, CO, USA, 2005, pp. 29-36.
[15]
J. Sivic, B. Russell, A. Efros, A. Zisserman, and W. Freeman, Discovering objects and their localization in images, in Proc. of International Conference on Computer Vision (ICCV), Beijing, China, 2005, pp. 370-377.
[16]
K. Yanai and K. Barnard, Probabilistic web image gathering, in Proc. of ACM SIGMM Workshop on Multimedia Information Retrieval (MIR), Singapore, 2005, pp. 57-64.
[17]
D. Lowe, Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision (IJCV), vol. 60, no. 2, pp. 91-110, November 2004.
[18]
A. Bosch, A. Zisserman, and X. Munoz, Scene classification via pLSA, in Proc. of 9th European Conference on Computer Vision (ECCV), Graz, Austria, 2006, pp. 517-530.
[19]
G. Csurka, C. Bray, C. Dance, and L. Fan, Visual categorization with bags of keypoints, in Proc. of Workshop on Statistical Learning in Computer Vision, Washington, DC, USA, 2004, pp. 1-22.
[20]
F. Li and P. Perona, A Bayesian hierarchical model for learning natural scene categories, in Proc. of Computer Vision and Pattern Recognition (CVPR), San Diego, CA, USA, 2005, pp. 524-531.
[21]
R. Fergus, P. Perona, and A. Zisserman, Object class recognition by unsupervised scale-invariant learning, in Proc. of Computer Vision and Pattern Recognition (CVPR), Madison, WI, USA, 2003, pp. 264-271.
[22]
Y. LeCun, F. Huang, and L. Bottou, Learning methods for generic object recognition with invariance to pose and lighting, in Proc. of Computer Vision and Pattern Recognition (CVPR), Washington, DC, USA, 2004, pp. 97-104.
[23]
K. Barnard, P. Duygulu, D. Forsyth, N.D. Freitas, D. Blei, and M. Jordan, Matching words and pictures, Journal of Machine Learning Research (JMLR), vol. 3, no. 1, pp. 1107-1135. August 2003.
[24]
K. Barnard and D. Forsyth, Learning the semantics of words and pictures, in Proc. of 8th IEEE International Conference on Computer Vision (ICCV), Vancouver, BC, USA, 2001, pp. 408-415.
[25]
C. Carson, M. Thomas, S. Belongie, J. Hellerstein, and J. Malik, Blobworld: A system for region-based image indexing and retrieval, in Proc. of 3rd International Conference on Visual Information Systems, Amsterdan, Netherland, 1999, pp. 509-516.
[26]
Y. Chen, J. Wang, and R. Krovetz, Content-based image retrieval by clustering, in Proc. of the 5th ACM SIGMM Workshop on Multimedia Information Retrieval (MIR), Berkeley, CA, USA, 2003, pp. 193-200.
[27]
Y. Deng, B. Manjunath, C. Kenney, M. Moore, and H. Shin, Efficient color representation for image retrieval, IEEE Transactions on Image Processing (TIP), vol. 10, no. 1, pp. 140-147, Jan. 2001.
[28]
J. Jeon, V. Lavrenko, and R. Manmatha, Automatic image annotation and retrieval using cross-media relevance models, in Proc. of the 26th ACM SIGIR Conference on Research and Development in Information Retrieval, Toronto, Canada, 2003, pp. 119-126.
[29]
E. Voutsakis, E. Petrakis, and E. Milios, Weighted link analysis for logo and trademark image retrieval on the web, in Proc. of IEEE International Conference on Web Intelligence (WI), Compiegne Cedex, France, 2005, pp. 581-585.
[30]
A. Joly and O. Buisson, Logo retrieval with a contrario visual query expansion, in Proc. of 17th ACM International Conference on Multimedia, Beijing, China, 2009, pp. 581-584.
[31]
R. Raina, A. Battle, H. Lee, B. Packer, and A. Ng, Self-taught learning: Transfer learning from unlabeled data, in Proc. of the 24th ACM International Conference on Machine Learning (ICML), Corvalis, OR, USA, 2007, pp. 766-773.
[32]
B. Olshausen and D. J. Field, Emergence of simple-cell receptive field properties by learning a sparse code for natural images, Nature, vol. 381, no. 6583, pp. 607-609, June 1996.
[33]
M. Morup, M. Schmidt, and L. Hansen, Shift invariant sparse coding of image and music data, http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/4659/pdf/imm4659.pdf, 2012.
[34]
X. Liu and B. Zhang, SISCHMAX: Discovering common contour patterns, in Proc. of the 9th IEEE International Conference on Cognitive Informatics (ICCI), Beijing, China, 2010, pp. 209-216.
[35]
H. Lee, A. Battle, R. Raina, and A. Ng, Efficient sparse coding algorithms, in Proc. of 20th Annual Conference on Neural Information Processing Systems (NIPS), Vancouver, BC, Canada, 2006, pp. 801-808.
[36]
J. Mutch and D. Lowe, Object class recognition and localization using sparse features with limited receptivefields, International Journal of Computer Vision (IJCV), vol. 80, no. 1, pp. 45-57, October 2008.
[37]
T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, and T. Poggio, Robust object recognition with cortex-like mechanisms, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol. 29, no. 3, pp. 411-426, March 2007.
Tsinghua Science and Technology
Pages 606-617
Cite this article:
Liu X, Zhang B. Automatic Collecting Representative Logo Images from the Internet. Tsinghua Science and Technology, 2013, 18(6): 606-617. https://doi.org/10.1109/TST.2013.6678906

401

Views

6

Downloads

6

Crossref

N/A

Web of Science

7

Scopus

0

CSCD

Altmetrics

Received: 25 September 2012
Revised: 12 June 2013
Accepted: 13 June 2013
Published: 06 December 2013
© The author(s) 2013
Return