TY - GEN
T1 - Automatic extraction and recognition of shoe logos with a wide variety of appearance
AU - Aoki, Kazunori
AU - Ohyama, Wataru
AU - Wakabayashi, Tetsushi
N1 - Publisher Copyright:
© 2017 MVA Organization All Rights Reserved.
PY - 2017/7/19
Y1 - 2017/7/19
N2 - A logo is a symbolic presentation that is designed not only to identify a product manufacturer but also to attract the attention of shoppers. Shoe logos are a challenging subject for automatic extraction and recognition using image analysis techniques because they have characteristics that distinguish them from those of other products, that is, there is much variation in the appearance of shoe logos. In this paper, we propose an automatic extraction and recognition method for shoe logos with a wide variety of appearanee using a limited number training samples. The proposed method employs maximally stable extremal regions (MSERs) for the initial region extraction, an iterative algorithm for region grouping, and gradient features and a support vector machine for logo recognition. The results of performance evaluation experiments using a logo dataset that consists of a wide variety of appearance show that the proposed method achieves promising performance for both logo extraction and recognition.
AB - A logo is a symbolic presentation that is designed not only to identify a product manufacturer but also to attract the attention of shoppers. Shoe logos are a challenging subject for automatic extraction and recognition using image analysis techniques because they have characteristics that distinguish them from those of other products, that is, there is much variation in the appearance of shoe logos. In this paper, we propose an automatic extraction and recognition method for shoe logos with a wide variety of appearanee using a limited number training samples. The proposed method employs maximally stable extremal regions (MSERs) for the initial region extraction, an iterative algorithm for region grouping, and gradient features and a support vector machine for logo recognition. The results of performance evaluation experiments using a logo dataset that consists of a wide variety of appearance show that the proposed method achieves promising performance for both logo extraction and recognition.
UR - http://www.scopus.com/inward/record.url?scp=85027870187&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85027870187&partnerID=8YFLogxK
U2 - 10.23919/MVA.2017.7986838
DO - 10.23919/MVA.2017.7986838
M3 - Conference contribution
AN - SCOPUS:85027870187
T3 - Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017
SP - 211
EP - 214
BT - Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IAPR International Conference on Machine Vision Applications, MVA 2017
Y2 - 8 May 2017 through 12 May 2017
ER -