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Dynamic Selection Feature Extractor for Trademark Retrieval

  • Simone B. K. Aires
  • Cinthia O. A. Freitas
  • Mauren L. Sguario
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11288)

Abstract

The paper contributes to the CBIR systems applied to trademark retrieval. The proposed method seeks to find dynamically the best feature extractor that represents the trademark queried. In the experiments are applied four feature extractors: Concavity/Convexity deficiencies (CC), Freeman Chain (FC), Scale Invariant Feature Transform (SIFT) and Hu Invariant Moments (Hu). These extractors represent a set of classes of features extractors, which are submitted to a classification process using two different classifiers: ANN (Artificial Neural Networks) and SVM (Support Vector Machines). The selecting the best feature extractor is important to processing the next levels in search of similar trademarks (i.e. applying zoning mechanisms or combining the best feature extractors), because it is possible restrict the number of operations in large databases. We carried out experiments using UK Patent Office database, with 10,151 images. Our results are in the same basis of the literature and the average in the best case for the normalized recall (Rn) is equal to 0.91. Experiments show that dynamic selection of extractors can contribute to improve the trademarks retrieval.

Keywords

Dynamic selection Feature extractor ANN SVM 

Notes

Acknowledgments

The authors wish to thanks the CAPES, Federal Technological University of Parana (UTFPR-PG, Brazil) and Pontifical Catholic University of Parana (PUCPR, Brazil), which have supported this work.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Universidade Tecnológica Federal do ParanáPonta GrossaBrazil
  2. 2.Pontifícia Universidade Católica do ParanáCuritibaBrazil

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