Content based image retrieval with sparse representations and local feature descriptors: A comparative study

Celik C., BİLGE H. Ş.

PATTERN RECOGNITION, vol.68, pp.1-13, 2017 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 68
  • Publication Date: 2017
  • Doi Number: 10.1016/j.patcog.2017.03.006
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1-13
  • Keywords: Content based image retrieval, Local feature descriptor, Sparse representation, Dictionary learning, Coefficient learning, FACE-RECOGNITION, OPTIMIZATION, COORDINATE, ALGORITHM, FRAMEWORK, MODEL
  • Gazi University Affiliated: Yes


Content Based Image Retrieval (CBIR) has been widely studied in the last two decades. Unlike text based image retrieval techniques, visual properties of images are used to obtain high level semantic information in CBIR. There is a gap between low level features and high level semantic information. This is called semantic gap and it is the most important problem in CBIR. The visual properties were extracted from low level features such as color, shape, texture and spatial information in early days. Local Feature Descriptors (LFDs) are more successful to increase performance of CBIR system. Then, a semantic bridge is built with high level semantic information. Sparse Representations (SRs) have become popular to achieve this aim in the last years.