A Novel Technique Based on Visual Words Fusion Analysis of Sparse Features for Effective Content-Based Image Retrieval

Muhammad Yousuf,Amjad Rehman,Muhammad Rashid,Hafiz Adnan Habib,Toqeer Mahmood,Tanzila Saba,Zahid Mehmood

doi:10.1155/2018/2134395

Abstract

Content-based image retrieval (CBIR) is a mechanism that is used to retrieve similar images from an image collection. In this paper, an effective novel technique is introduced to improve the performance of CBIR on the basis of visual words fusion of scale-invariant feature transform (SIFT) and local intensity order pattern (LIOP) descriptors. SIFT performs better on scale changes and on invariant rotations. However, SIFT does not perform better in the case of low contrast and illumination changes within an image, while LIOP performs better in such circumstances. SIFT performs better even at large rotation and scale changes, while LIOP does not perform well in such circumstances. Moreover, SIFT features are invariant to slight distortion as compared to LIOP. The proposed technique is based on the visual words fusion of SIFT and LIOP descriptors which overcomes the aforementioned issues and significantly improves the performance of CBIR. The experimental results of the proposed technique are compared with another proposed novel features fusion technique based on SIFT-LIOP descriptors as well as with the state-of-the-art CBIR techniques. The qualitative and quantitative analysis carried out on three image collections, namely, Corel-A, Corel-B, and Caltech-256, demonstrate the robustness of the proposed technique based on visual words fusion as compared to features fusion and the state-of-the-art CBIR techniques.

Highlights

Image retrieval on the basis of image contents has been a vigorous area of research in the last three decades [1]
In order to reduce the computational cost of the proposed technique that is slightly increased due to visual words fusion as well as the features fusion of scaleinvariant feature transform (SIFT) and local intensity order pattern (LIOP) feature descriptors, performance analysis is carried out using different features percentages per image as reported in the subsequent sections
In order to demonstrate the robustness of the proposed technique based on visual words fusion of SIFT and LIOP descriptors, its mean average precision (MAP) performance is compared with the MAP performance of the proposed technique based on features fusion as well as with the state-of-the-art Content-based image retrieval (CBIR) techniques [36, 41,42,43,44], whose experimental details are shown in Figure 4 and Table 2

Summary

Introduction

Image retrieval on the basis of image contents has been a vigorous area of research in the last three decades [1]. Many approaches have been introduced regarding image retrieval on the basis of image contents [2, 3]. A text-based image retrieval system has two issues. Assigning keywords for image annotation is subjective. These two drawbacks led to the development of a new system, which is CBIR [2]. CBIR aims to develop techniques which can be used for extracting similar images from image archives. Current CBIR methods are further categorized as global and local features [1, 4, 5]

Methods

Results

Conclusion