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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.

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.

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.

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.

Although the use of online technology has generated excitement over its potential to increase access to education, most existing research has focused on comparing online and in-person student performance. This study provides empirical evidence that semiprivate space at the family level affects students' access to online education.

Students from 1,565 low-income households in China were surveyed, and a multivariate logistic regression analysis was conducted on the hypothesized factors that affect access to online education at home.

The results show that the absence of computers, smartphones and broadband networks at home severely hinders children's access to online education, and even leads to their exclusion from it. Children with their fathers or paternal grandfathers as guardians have a lower probability of receiving online education at home. It was also found that the higher the education level of the head of the household, the more likely it is that children will receive online education at home.

This study is one of the first to examine online accessibility at the family level. It also demonstrates that the semiprivate space at the family level may limit opportunities for students who would otherwise pursue online education at home.