Object-Based Image Retrieval Using the U-Net-Based Neural Network.

Sandeep Kumar,Ambuj Kumar Agarwal,Arpit Jain,Anshu Ghimire,Shilpa Rani,Suneet Kumar Gupta

doi:10.1155/2021/4395646

Abstract

Day by day, all the research communities have been focusing on digital image retrieval due to more internet and social media uses. In this paper, a U-Net-based neural network is proposed for the segmentation process and Haar DWT and lifting wavelet schemes are used for feature extraction in content-based image retrieval (CBIR). Haar wavelet is preferred as it is easy to understand, very simple to compute, and the fastest. The U-Net-based neural network (CNN) gives more accurate results than the existing methodology because deep learning techniques extract low-level and high-level features from the input image. For the evaluation process, two benchmark datasets are used, and the accuracy of the proposed method is 93.01% and 88.39% on Corel 1K and Corel 5K. U-Net is used for the segmentation purpose, and it reduces the dimension of the feature vector and feature extraction time by 5 seconds compared to the existing methods. According to the performance analysis, the proposed work has proven that U-Net improves image retrieval performance in terms of accuracy, precision, and recall on both the benchmark datasets.

Highlights

Nowadays, digital image techniques lead to the tremendous usage of the image retrieval process on the internet. e image retrieval system retrieves different images over the internet with different captions and labels under each image stored in the database
An image retrieval system that uses content as a search key for browsing is known as content-based image retrieval (CBIR) [1]. e main goal of the CBIR methodology is to extract meaningful information from images such as color shape and texture for effective retrieval. e research community contributed to CBIR in the direction of image properties, relevance feedback, fuzzy color, and texture histogram [2]. e proposed algorithms, color histogram, based on relevant image retrieval (CHRIR) [3, 4], work with the image’s low-level features, such as objects’ physical features for image retrieval. These visual features might not reveal the proper semantics of the image. ese algorithms may not suit and may generate erroneous results when considering content images in a broad database. erefore, to improve the CBIR system’s accuracy, region-based image retrieval methods using image U-Net-based segmentation were introduced [5]: (i) Haar discrete wavelet transform (H-DWT) is a popular transformation technique that transforms any image from the spatial domain to frequency domain. e wavelet transformation method represents a function as a family of essential functions termed wavelets [2, 6, 7]
Conclusion e U-Net-based architecture makes the proposed work different from the existing methods, giving a high detection rate. e U-Net-based neural network detects the object more efficiently. It is a fully convolutional neural network (CNN) that works with very few training models yet yields compelling segmentation results

Summary

Introduction

Digital image techniques lead to the tremendous usage of the image retrieval process on the internet. e image retrieval system retrieves different images over the internet with different captions and labels under each image stored in the database. E proposed algorithms, color histogram, based on relevant image retrieval (CHRIR) [3, 4], work with the image’s low-level features, such as objects’ physical features for image retrieval. These visual features might not reveal the proper semantics of the image. Haar wavelet is used to represent an image by computing the wavelet transform. It involves low-pass filtering as well as highpass filtering operations simultaneously [8]. Haar wavelet’s function X(t) can be described as

Methods

Results

Conclusion