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Improved image retrieval and classification with combined invariant features and color descriptor

  • Yong-Hwan Lee
  • Sung-Il BangEmail author
Original Research

Abstract

As the quantity of digital images grows in many applications in our daily life, users experience an increased difficulty in finding relevant images within their image collections and common image repositories. This paper proposes a novel image search scheme that extracts the features of an image using a combined invariant features and color description to retrieve specific images using query-by-example. The proposed method can be executed in real-time on an iPhone, and can be easily used to identify a natural color image with its invariant visual features. The proposed scheme is evaluated by assessing the performance of a simulation in terms of the average precision and F-score in image databases that are commonly used for image retrieval. The experimental results reveal that the proposed algorithm offers a significant improvement of more than 7.35 and 18.09% in retrieval effectiveness when compared to open source OpenSURF and MPEG-7 color and texture descriptor, respectively. The main contribution of this paper is that the proposed approach achieves a high accuracy and stability by using a combination of the improved SURF and color descriptor when searching for a natural image.

Keywords

Image retrieval Image search Speeded-up robust feature (SURF) Color layout descriptor Locality sensitive hashing (LSH) 

Notes

Acknowledgements

The present research was conducted by the research fund of Dankook University in 2015.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Digital ContentsWonkwang UniversityIksanSouth Korea
  2. 2.Department of Electrical and Electronic EngineeringDankook UniversityYonginSouth Korea

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