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…
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.
The purpose of this paper is to examine a possible negative spillover effect in sports sponsorship to answer whether the sponsored team’s poor performance will have a…
The purpose of this paper is to examine a possible negative spillover effect in sports sponsorship to answer whether the sponsored team’s poor performance will have a negative effect on audiences’ trust in its sponsor’s brand. The authors further analysed whether the audience’s attitude towards the team plays a mediating role and whether the audience’s personality type (active vs passive) plays a moderating role in this negative spillover effect.
Three experimental studies were conducted with 380 Chinese undergraduates and MBA student participants over two years. The authors designed the experiment as a computer-mediated intervention in which good, poor and neutral performance groups were compared. After the respondents were exposed to the intervention, we asked them to answer questions using a computer terminal. We analysed the data from the three experiments through analysis of variance (ANOVA), regression analysis and a bootstrap.
The audiences who were exposed to a team’s poor performance condition reported less trust in the sponsor’s brand relative to those exposed to a good performance condition, and the brand trust was even lower than for those who were exposed to a control condition (no performance information). Further, the audience’s negative attitude towards the sports team mediated the negative effect of the team’s poor performance on its sponsor’s brand trust. The negative effect was more obvious for individuals with Type A personalities (active) than for those with Type B personalities (passive).
The prior literature has neglected a possible negative effect of a sports team’s performance on its sponsor’s brand trust. In particular, questions of whether, how and when this negative effect occurs are critical for sponsors, teams, and audiences. Since sports team sponsorship is burgeoning in China, the negative implications are unclear in this new context. Thus, the revelation that the negative spillover effects of a team’s poor performance on audiences’ trust in the sponsor’s brand provides two original contributions. First, the negative effect reveals value for multiple sponsorship stakeholders. Second, the Chinese context in this study adds value for future research and practice regarding both Chinese-foreign and domestic Chinese decisions.