| Autor: |
Palai, Charulata, Jena, Pradeep Kumar, Khuntia, Bonomali, Mishra, Tapas Kumar, Pattanaik, Satya Ranjan |
| Předmět: |
|
| Zdroj: |
Revue d'Intelligence Artificielle; Feb2023, Vol. 37 Issue 1, p63-72, 10p |
| Abstrakt: |
This work proposes a novel content-based image retrieval framework using adaptive weight feature fusion in the International Commission on Illumination (CIE) color space. To enhance the weights of the saliency region features of an image, an adaptive wrapper model is proposed for the adaptive feature selection. Initially, the images are transferred to the CIE color space, i.e., the L*, a*, b* color space. The local binary model (LBP) texture features of all four channels are analyzed class-wise. For each class, the weights of the LBP features for a* and b* axis are calculated dynamically as per their class variance. The weighted LBP features along a* and b* axis are merged, which is referred to as the LBPCW feature in the CIE color space. To test the performance of the proposed LBPCW feature we developed a CBIR system, here two standard classifiers i.e. Support Vector Machine (SVM), and Naïve Bayes (NB) is used for classification and Euclidian distance measure is used for image retrieval. The model is tested with two public datasets Wang-1K and Corel-5K. It is observed that our proposed LBPCW feature outperforms LBP and local binary pattern with saliency map (LBPSM) features. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
