Extraction and Recognition of Shoe Logos with a Wide Variety of Appearance Using Two-Stage Classifiers

Kazunori AOKI

IEICE TRANSACTIONS on Information and Systems   Vol.E101-D    No.5    pp.1325-1332
Publication Date: 2018/05/01
Publicized: 2018/02/16
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017MVP0026
Type of Manuscript: Special Section PAPER (Special Section on Machine Vision and its Applications)
Category: Machine Vision and its Applications
logo extraction,  logo recognition,  maximally stable extremal regions,  gradient histogram features,  support vector machine,  

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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 within-class 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 appearance using a limited number of training samples. The proposed method employs maximally stable extremal regions 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 appearances show that the proposed method achieves promising performance for both logo extraction and recognition.