Logo detection with extendibility and discrimination

Kuo-Wei Li, Shu Yuan Chen, Songzhi Su, Derjyh Duh, Hongbo Zhang, Shaozi Li

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

Logos are specially designed marks that identify goods, services, and organizations using distinguished characters, graphs, signals, and colors. Identifying logos can facilitate scene understanding, intelligent navigation, and object recognition. Although numerous logo recognition methods have been proposed for printed logos, a few methods have been specifically designed for logos in photos. Furthermore, most recognition methods use codebook-based approaches for the logos in photos. A codebook-based method is concerned with the generation of visual words for all the logo models. When new logos are added, the codebook reconstruction is required if effectiveness is a crucial factor. Moreover, logo detection in natural scenes is difficult because of perspective tilt and non-rigid deformation. Therefore, this study develops an extendable, but discriminating, model-based logo detection method. The proposed logo detection method is based on a support vector machine (SVM) using edge-based histograms of oriented gradient (HOGE) as features through multi-scale sliding window scanning. Thereafter, anti-distortion affine scale invariant feature transform (ASIFT) is used for logo verification with constraints on the ASIFT matching pairs and neighbors. The experimental results using the public Flickr-Logo database confirm that the proposed method has a higher retrieval and precision accuracy compared to existing model-based methods.
Original languageAmerican English
Pages (from-to)1285–1310
Number of pages26
JournalMultimedia Tools and Applications
Volume72
Issue number2
DOIs
StatePublished - Sep 2014

Keywords

  • Logo detection
  • Logo recognition
  • Edge-based histogram of oriented gradient
  • Affine scale invariant feature transform
  • Multi-scale sliding window scanning

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