New content-based image retrieval system based on optimised integration of DCD, wavelet and curvelet features

  • Fadaei S
  • Amirfattahi R
  • Ahmadzadeh M
  • 16


    Mendeley users who have this article in their library.
  • 4


    Citations of this article.


? The Institution of Engineering and Technology 2016.A new content-based image retrieval (CBIR) scheme is proposed based on the optimised combination of the colour and texture features to enhance the image retrieval precision. This work focuses on a uniform partitioning scheme which is applied in the Hue, Saturation and Value (HSV) colour space to extract dominant colour descriptor (DCD) features. In the proposed CBIR scheme, the DCD features are initially extracted as the colour features, and then an appropriate similarity measure is applied. Also, several wavelet and curvelet features are defined as texture features to overcome the noise and the problem of image translation. Finally, the colour and texture features are optimally combined by using the particle swarm optimisation algorithm. The findings show that not only the proposed colour, wavelet and curvelet features outperform the existing ones but also their optimum combination has a better accuracy in comparison with several contemporary CBIR systems. The performance analysis shows that the proposed method improves the average precision metric from 67.85 to 71.05% for DCD, 58.90 to 65.43% for wavelet and 53.18 to 56.00% for curvelet using Corel dataset. In addition, the optimum combination presents the average precision of %76.50 which is significantly higher than the other state-of-the-art methods.

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


  • Sadegh Fadaei

  • Rassoul Amirfattahi

  • Mohammad Reza Ahmadzadeh

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free