Image Retrieval Method Based on Image Feature Fusion and Discrete Cosine Transform

Dayou Jiang,Jongweon Kim

doi:10.3390/app11125701

Abstract

This paper presents a new content-based image retrieval (CBIR) method based on image feature fusion. The deep features are extracted from object-centric and place-centric deep networks. The discrete cosine transform (DCT) solves the strong correlation of deep features and reduces dimensions. The shallow features are extracted from a Quantized Uniform Local Binary Pattern (ULBP), hue-saturation-value (HSV) histogram, and dual-tree complex wavelet transform (DTCWT). Singular value decomposition (SVD) is applied to reduce the dimensions of ULBP and DTCWT features. The experimental results tested on Corel datasets and the Oxford building dataset show that the proposed method based on shallow features fusion can significantly improve performance compared to using a single type of shallow feature. The proposed method based on deep features fusion can slightly improve performance compared to using a single type of deep feature. This paper also tests variable factors that affect image retrieval performance, such as using principal component analysis (PCA) instead of DCT. The DCT can be used for dimensional feature reduction without losing too much performance.

Highlights

The jPlaDctHld method fused with shallow features slightly improves the performance comcurve based on the deep feature is relatively flat
The numbers of retrieved (NR) is 20, and the Canberra metric is used for the similarity measure
The experimental results above show that using principal component analysis (PCA) for dimensional feature reduction has better performance for each dataset than discrete cosine transform (DCT)

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. As the amount of available data grows, the explosion of images has brought significant challenges to automated image search and retrieval. While the explosive growth of internet data provides richer information and more choices, it means that users need to browse an ever-increasing amount of content to retrieve the desired results. Effectively integrating multiple-media information on the internet to assist users in quick and efficient retrieval has become a new hot issue in image retrieval

Methods

Results

Discussion

Conclusion