Szczegóły publikacji
Opis bibliograficzny
Integration of discrete wavelet transform, DBSCAN, and classifiers for efficient content based image retrieval / Muhammad Junaid Khalid, Muhammad Irfan, Tariq Ali, Muqaddas Gull, Umar Draz, Adam GŁOWACZ, Maciej Sulowicz, Arkadiusz Dziechciarz, Fahad Salem AlKahtani, Shafiq Hussain // Electronics [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2079-9292. — 2020 — vol. 9 iss. 11 art. no. 1886, s. 1–15. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 13–15, Abstr. — Publikacja dostępna online od: 2020-11-09
Autorzy (10)
- Khalid Muhammad Junaid
- Irfan Muhammad
- Ali Tariq
- Gull Muqaddas
- Draz Umar
- AGHGłowacz Adam
- Sulowicz Maciej
- Dziechciarz Arkadiusz
- AlKahtani Fahad Salem
- Hussain Shafiq
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 131000 |
|---|---|
| Data dodania do BaDAP | 2020-11-12 |
| Tekst źródłowy | URL |
| DOI | 10.3390/electronics9111886 |
| Rok publikacji | 2020 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Electronics |
Abstract
In the domain of computer vision, the efficient representation of an image feature vector for the retrieval of images remains a significant problem. Extensive research has been undertaken on Content-Based Image Retrieval (CBIR) using various descriptors, and machine learning algorithms with certain descriptors have significantly improved the performance of these systems. In this proposed research, a new scheme for CBIR was implemented to address the semantic gap issue and to form an efficient feature vector. This technique was based on the histogram formation of query and dataset images. The auto-correlogram of the images was computed w.r.t RGB format, followed by a moment’s extraction. To form efficient feature vectors, Discrete Wavelet Transform (DWT) in a multi-resolution framework was applied. A codebook was formed using a density-based clustering approach known as Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The similarity index was computed using the Euclidean distance between the feature vector of the query image and the dataset images. Different classifiers, like Support Vector (SVM), K-Nearest Neighbor (KNN), and Decision Tree, were used for the classification of images. The set experiment was performed on three publicly available datasets, and the performance of the proposed framework was compared with another state of the proposed frameworks which have had a positive performance in terms of accuracy.