Retrieval and recognition of faces using content-based image retrieval (CBIR) and feature combination method

Devi, Ningthoujam Sunita; Hemachandran, K.

doi:10.1109/iccic.2016.7919577

Cited by 2 publications

(2 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Computational complexity, retrieval speed and accuracy are often used in retrieval to determine the quality of image retrieval algorithms. Precision and recall are currently the most widely used evaluation criteria in CBIR (Celik and Bilge, 2017; Devi and Hemachandran, 2017). The precision and recall formulas are as follows: …”

Section: Retrieval Performance Evaluationmentioning

confidence: 99%

“…The CBIR emerged in the 1980s and has been applied in an increasingly broader range of applications for exploring image retrieval systems (Liu et al , 2011; Rafiee et al , 2010), such as the Retrieval Ware system (Dowe, 1993), the Photobook (Pentland et al , 1996) search engine and the query by image and video content (QBIC) system (Flickner et al , 2002). The CBIR performs image retrieval based on the similarity measure of image features extracted from image content, which makes image retrieval more descriptive and intuitive than text-based approaches (Chun et al , 2008; Devi and Hemachandran, 2017). The retrieval performance of a CBIR system crucially depends on feature extraction and similarity measurements that have been extensively studied for decades (Saritha et al , 2018; Juneja et al , 2015; Sharif et al , 2018).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Wang

Han

Jin

2019

View full text Add to dashboard Cite

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.

show abstract