Integration of Discrete Wavelet Transform, DBSCAN, and Classifiers for Efficient Content Based Image Retrieval

Muhammad Junaid Khalid,Maciej Sulowicz,Arkadiusz Dziechciarz,Muqaddas Gull,Fahad Salem Alkahtani,Adam Glowacz,Shafiq Hussain,Muhammad Irfan,Tariq Ali,Umar Draz

doi:10.3390/electronics9111886

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.

Highlights

In multimedia research, with the incessant evolution in facet and mid-level vision, the retrieval of the desired image efficiently and accurately from a dataset has been in the domain of Content-BasedImage Retrieval (CBIR)
We aimed to further improve the performance of the proposed approach by conducting more discerning research with larger datasets and better feature vector formation
A novel approach in which a feature vector was formed, visually similar images retrieved based on the query and database image feature vector similarity index, was tested

Summary

Introduction

With the incessant evolution in facet and mid-level vision, the retrieval of the desired image efficiently and accurately from a dataset has been in the domain of Content-BasedImage Retrieval (CBIR). With the incessant evolution in facet and mid-level vision, the retrieval of the desired image efficiently and accurately from a dataset has been in the domain of Content-Based. The efficient representation of image features and cutting of the semantic gap remains a significant problem in Image Retrieval (IR) tasks. The proposed approach is the interfusion of image features in a multilayer exploration context for CBIR, that has been influenced by wavelets. The query images Discrete Wavelet Transform (DWT) coefficients computation was performed. Extensive experiments were conducted on Corel datasets, and accomplishments of the proposed methodology were measured using certain performance evaluation matrices, like precision and recall. The proposed approach was implemented using matrix laboratory (MATLAB)

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