A Smart Content-Based Image Retrieval Approach Based on Texture Feature and Slantlet Transform


  • Hakeem Imad Mhaibes Middle Technical University, Computer Center, Kut Technical Institute Muaskir Alrasheed, Baghdad, Iraq
  • Qahtan Makki Shallal Southern Technical University, Iraq Faculty of Electrical Engineering, Basra Management Technical College Basrah, Iraq
  • May Hattim Abood Al Iraqia University, Computer Engineering Department, College of Engineering, Baghdad, Iraq




Image Processing, Information Retreival, Slantlet Trasnform, CBIR, Feature Extraction, Similarity measure


With the advancement of digital storing and capturing technologies in recent years, an image retrieval system has been widely known for Internet usage. Several image retrieval methods have been proposed to find similar images from a collection of digital images to a specified query image. Content-based image retrieval (CBIR) is a subfield of image retrieval techniques that extracts features and descriptions content such as color, texture, and shapes from a huge database of images. This paper proposes a two-tier image retrieval approach, a coarse matching phase, and a fine-matching phase. The first phase is used to extract spatial features, and the second phase extracts texture features based on the Slantlet transform. The findings of this study revealed that texture features are reliable and capable of producing excellent results and unsusceptible to low resolution and proved that the SLT-based texture feature is the perfect mate. The proposed method's experimental results have outperformed the benchmark results with precision gaps of 28.0 % for the Caltech 101 dataset. The results demonstrate that the two-tier strategy performed well with the successive phase (fine-matching) and the preceding phase (coarse matching) working hand in hand harmoniously.




How to Cite

H. Mhaibes, Qahtan Makki Shallal, and May Hattim Abood, “A Smart Content-Based Image Retrieval Approach Based on Texture Feature and Slantlet Transform”, IJECES, vol. 13, no. 8, pp. 621-631, Oct. 2022.



Original Scientific Papers