Open AccessContent‐based image retrieval system via sparse representation
Author(s) -
Mohamadzadeh Sajad,
Farsi Hassan
Publication year - 2016
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2015.0165
Subject(s) - computer science , image retrieval , sparse approximation , artificial intelligence , pattern recognition (psychology) , representation (politics) , precision and recall , visual word , image (mathematics) , feature (linguistics) , automatic image annotation , wavelet transform , content based image retrieval , wavelet , linguistics , philosophy , politics , political science , law
The aim of image retrieval systems is to automatically assess, retrieve and represent relative images‐based user demand. However, the accuracy and speed of image retrieval are still an interesting topic of many researches. In this study, a new method based on sparse representation and iterative discrete wavelet transform has been proposed. To evaluate the applicability of the proposed feature‐based sparse representation for image retrieval technique, the precision at percent recall and average normalised modified retrieval rank are used as quantitative metrics to compare different methods. The experimental results show that the proposed method provides better performance in comparison with other methods.