Noise Resilient Local Gradient Orientation for Content-Based Image Retrieval

Samina Bilquees,Muhammad Tariq Mahmood,Hassan Dawood,Nadeem Majeed,Ali Javed,Hussain Dawood,E Bernabeu

doi:10.1155/2021/4151482

Abstract

In a world of multimedia information, where users seek accurate results against search query and demand relevant multimedia content retrieval, developing an accurate content-based image retrieval (CBIR) system is difficult due to the presence of noise in the image. The performance of the CBIR system is impaired by this noise. To estimate the distance between the query and database images, CBIR systems use image feature representation. The noise or artifacts present within the visual data might confuse the CBIR when retrieving relevant results. Therefore, we propose Noise Resilient Local Gradient Orientation (NRLGO) feature representation that overcomes the noise factor within the visual information and strengthens the CBIR to retrieve accurate and relevant results. The proposed NRLGO consists of three steps: estimation and removal of noise to protect the local visual structure; extraction of color, texture, and local contrast features; and, at the end, generation of microstructure for visual representation. The Manhattan distance between the query image and the database image is used to measure their similarity. The proposed technique was tested using the Corel dataset, which contains 10000 images from 100 different categories. The outcomes of the experiment signify that the proposed NRLGO has higher retrieval performance in comparison with state-of-the-art techniques.

Highlights

In a world of multimedia information, where users seek accurate results against search query and demand relevant multimedia content retrieval, developing an accurate content-based image retrieval (CBIR) system is difficult due to the presence of noise in the image. e performance of the CBIR system is impaired by this noise
Experiment is performed on Corel dataset to evaluate the retrieval performance of Noise Resilient Local Gradient Orientation (NRLGO) method. e performance of NRLGO is given as follows
NRLGO extracts noise-robust features that remain invariant to variation in scale, illumination, and orientation

Summary

Introduction

In a world of multimedia information, where users seek accurate results against search query and demand relevant multimedia content retrieval, developing an accurate content-based image retrieval (CBIR) system is difficult due to the presence of noise in the image. e performance of the CBIR system is impaired by this noise. In a world of multimedia information, where users seek accurate results against search query and demand relevant multimedia content retrieval, developing an accurate content-based image retrieval (CBIR) system is difficult due to the presence of noise in the image. Erefore, we propose Noise Resilient Local Gradient Orientation (NRLGO) feature representation that overcomes the noise factor within the visual information and strengthens the CBIR to retrieve accurate and relevant results. Soft Histogram of Edge Local Orientation (S-HELO) uses local orientation computation for improving the performance of sketch-based image retrieval [7]. We propose Noise Resilient Local Gradient Orientation (NRLGO) that overcomes the noise factor within the visual information and strengthens the CBIR to retrieve accurate and relevant results. In our proposed NRLGO, the color, texture, and local contrast information were used for feature representation of visual data. NRLGO has noise resilience characteristics and saves the local structures of visual information from noise by finding an uncertain state. e major contribution of NRLGO is summarized as follows:

Results

Discussion

Conclusion