Human, Object and Scene Centric Image Retrieval Engine to Enhance Image Management

Naveed, Humza; Khan, Gulraiz; Khan, Zahid; Khan, Muhammad Usman Ghani

doi:10.1109/fit.2017.00025

Cited by 2 publications

(1 citation statement)

References 35 publications

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“…In recent years, with the rapid development of global information technology and the accelerated popularization of artificial intelligence applications, image retrieval has become important to applications in many fields such as internet technology (Naveed et al , 2018), science education (Guzmán et al , 2017), aeronautics and astronautics (Bondur and Murynin, 2016), national defense and military (Sarkar and Acton, 2016), medicine and health (Muramatsu, 2018), traffic management (Zhang et al , 2017) and agricultural production (Kebapc and Kebapci, 2012). It has also become one of the most important research directions in the field of artificial intelligence.…”

Section: Introductionmentioning

confidence: 99%

Review of image low-level feature extraction methods for content-based image retrieval

Wang

Han

Jin

2019

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Purpose In the past few decades, the content-based image retrieval (CBIR), which focuses on the exploration of image feature extraction methods, has been widely investigated. The term of feature extraction is used in two cases: application-based feature expression and mathematical approaches for dimensionality reduction. Feature expression is a technique of describing the image color, texture and shape information with feature descriptors; thus, obtaining effective image features expression is the key to extracting high-level semantic information. However, most of the previous studies regarding image feature extraction and expression methods in the CBIR have not performed systematic research. This paper aims to introduce the basic image low-level feature expression techniques for color, texture and shape features that have been developed in recent years. Design/methodology/approach First, this review outlines the development process and expounds the principle of various image feature extraction methods, such as color, texture and shape feature expression. Second, some of the most commonly used image low-level expression algorithms are implemented, and the benefits and drawbacks are summarized. Third, the effectiveness of the global and local features in image retrieval, including some classical models and their illustrations provided by part of our experiment, are analyzed. Fourth, the sparse representation and similarity measurement methods are introduced, and the retrieval performance of statistical methods is evaluated and compared. Findings The core of this survey is to review the state of the image low-level expression methods and study the pros and cons of each method, their applicable occasions and certain implementation measures. This review notes that image peculiarities of single-feature descriptions may lead to unsatisfactory image retrieval capabilities, which have significant singularity and considerable limitations and challenges in the CBIR. Originality/value A comprehensive review of the latest developments in image retrieval using low-level feature expression techniques is provided in this paper. This review not only introduces the major approaches for image low-level feature expression but also supplies a pertinent reference for those engaging in research regarding image feature extraction.

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