Volume 14, Issue 2 (June 2018)                   IJEEE 2018, 14(2): 153-161 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Tannaz S, Sedghi T. Image Retrieval Using Dynamic Weighting of Compressed High Level Features Framework with LER Matrix. IJEEE 2018; 14 (2) :153-161
URL: http://ijeee.iust.ac.ir/article-1-1171-en.html
Image Retrieval Using Dynamic Weighting of Compressed High Level Features Framework with LER Matrix
S. Tannaz , T. Sedghi
Abstract:   (3955 Views)
In this article, a fabulous method for database retrieval is proposed.  The multi-resolution modified wavelet transform for each of image is computed and the standard deviation and average are utilized as the textural features. Then, the proposed modified bit-based color histogram and edge detectors were utilized to define the high level features. A feedback-based dynamic weighting of shape, color and textural features composition produce a resistant feature vectors for image retrieval and recall. A comprehensive and unified matching scheme based on matrix error rate technique was accomplished for similarity of image and retrieval procedure. The feature vectors size in our algorithm is the least one evaluated to the different techniques. Furthermore, the calculation time of previously published techniques is much more than the presented algorithm which is a benefit in proposed retrieval method. The experimental results illustrates that novel algorithm obtains more precious in retrieval and the efficiency in evaluating with the other techniques and algorithms at Corel color image database.
Keywords: Feature Generation , Retrieval , Recall , LER Matrix , Pattern Recognition
Full-Text [PDF 1189 kb]   (1389 Downloads)    
Type of Study: Research Paper | Subject: ArtificialIntelligence
Received: 2017/09/25 | Revised: 2018/06/17 | Accepted: 2017/12/26
Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Creative Commons License
© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.