Qijin Chen, Jituo Li, Zheng Liu, Guodong Lu, Xinyu Bi and Bei Wang
Clothing retrieval is very useful to help the clients to efficiently search out the apparel they want. Currently, the mainstream clothing retrieval methods are attribute semantics…
Abstract
Purpose
Clothing retrieval is very useful to help the clients to efficiently search out the apparel they want. Currently, the mainstream clothing retrieval methods are attribute semantics based, which however are inconvenient for common clients. The purpose of this paper is to provide an easy‐to‐operate apparels retrieval mode with the authors' novel approach of clothing image similarity measurement.
Design/methodology/approach
The authors measure the similarity between two clothing images by computing the weighted similarities between their bundled features. Each bundled feature consists of the point features (SIFT) which are further quantified into local visual words in a maximally stable extremal region (MSER). The authors weight the importance of bundled features by the precision of SIFT quantification and local word frequency that reflects the frequency of the common visual words appeared in two bundled features. The bundled features similarity is computed from two aspects: local word frequency; and SIFTs distance matrix that records the distances between every two SIFTs in a bundled feature.
Findings
Local word frequencies improves the recognition between two bundled features with the same common visual words but different local word frequency. SIFTs distance matrix has the merits of scale invariance and rotation invariance. Experimental results show that this approach works well in the situations with large clothing deformation, background exchange and part hidden, etc. And the similarity measurement of Weight+Bundled+LWF+SDM is the best.
Originality/value
This paper presents an apparel retrieval mode based on local visual features, and presents a new algorithm for bundled feature matching and apparel similarity measurement.
Details
Keywords
Zheng Liu, Jin Wang, Qijin Chen and Guodong Lu
To enhance the pleasure experience of clothes shopping online, finding satisfactory clothing and similar clothing recommendations to customers should be available and accurate…
Abstract
Purpose
To enhance the pleasure experience of clothes shopping online, finding satisfactory clothing and similar clothing recommendations to customers should be available and accurate. The purpose of this paper is to present a method for automatically computing the similarity between two apparels and giving an effective recommendation.
Design/methodology/approach
Based on a tabular layout of article characteristics the authors built a clothing information model to describe clothing. The clothing attributes are classified according to excavating features of the model. After the proposal of the computation algorithm for various attributes, an efficient similarity computation method is developed to obtain similar clothes with the given cloth. To prevent error and information omission during the computation, the analytic hierarchy process method and entropy method are adopted by the integrated weights as a control.
Findings
Clothing is a non‐rigid product which has a lot of crossover and complicated attributes and features. This paper found a tabular layout of article characteristics can explain the clothing clearly. Through experiments the authors found the weight of attributes to have a great influence on similar results during the similarity computation.
Originality/value
This paper presents a new way to describe clothing information, and present the algorithm for attributes computation.
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Wen-Hong Liu, Paohsi Wang, Hsiao-Chien Lee, Yi-Juen Chen, Ayling Huang, Yung-Chuan Ko, Chun-Cheng Kuo and Chih-Cheng Lin
This study aims to examine the efforts of two universities in Taiwan, particularly their engagement in coastal rural communities, and provides insights into the substantial…
Abstract
Purpose
This study aims to examine the efforts of two universities in Taiwan, particularly their engagement in coastal rural communities, and provides insights into the substantial contributions of University Social Responsibility (USR) projects toward achieving the sustainable development goals (SDGs) in rural settings. The objective of this research is to analyze the outcomes of these case projects on the SDGs and, consequently, to propose a theoretical model aimed at enhancing student participation in achieving SDGs through USR programs.
Design/methodology/approach
This study adapted the methodology comprising SDGs contribution analysis and participant observation. The SDGs contribution analysis unfolds in three steps. First, the authors conducted evaluation of the 2030 Agenda for Sustainable Development. This step involved a thorough analysis of all 17 SDGs and their respective targets to establish a foundational understanding. Second, analyzed the outcomes of the case projects to examine the specific contributions of case projects toward achieving the SDGs. Third, the information from the preceding steps is analyzed to determine the extent of the case projects’ contributions to the SDGs.
Findings
The study proposes a theoretical model to enhance student engagement in achieving SDGs, emphasizing student empowerment, community partnership and robust evaluation methods aligned with SDGs and social impacts. This model could offer guidance for higher education institutions (HEIs) globally on utilizing USR programs to contribute to the SDGs, while simultaneously enriching student learning experiences through practical engagement and empowerment.
Research limitations/implications
This model can be enhanced and validated by applying more rigorous scientific methods. For instance, conducting surveys on students and community participants of events and activities, utilizing a statistically rigorous approach such as pre-post testing, can analyze the effectiveness of these programs on various SDG-related variables (e.g. awareness of SDGs). Additionally, exploring the relationships between the tested variables can be a potential research direction. For instance, examining whether community engagement can positively increase the social impacts of USR projects, or whether student empowerment can enhance community capacity building, are important issues worthy of discovery.
Practical implications
This model emphasizes the pivotal role of student empowerment, advocating for an educational approach that not only enhances students’ proficiency in community development but also potentially shapes their career trajectories, as evidenced by the case projects examined in this study. In essence, this model offers HEIs a structured pathway to enrich student engagement in realizing SDGs through USR initiatives. It posits student empowerment as the foundational element, fostering a learning environment where students gain valuable skills and insights into community development, potentially guiding their future professional endeavors. This research provides practical direction for those HEIs implementing USR projects, which will increase the positive impacts brought by HEIs, especially for the students and local community.
Originality/value
To the best of the authors’ knowledge, no previous studies have proposed a theoretical model specifically designed to engage students in achieving SDGs through USR programs in a rural context. The significance of this study lies in its potential to serve as a guide for higher education institutions globally, enabling them to effectively leverage USR programs to contribute toward SDGs. This makes the study an invaluable resource for researchers, policymakers and educators who are committed to fostering sustainability.
Details
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The research is made in view of the anthropometry information obtaining problem in garment MTM on the network mode. The purpose of this paper is to obtain anthropometry…
Abstract
Purpose
The research is made in view of the anthropometry information obtaining problem in garment MTM on the network mode. The purpose of this paper is to obtain anthropometry information in a convenient and detailed way in garment MTM on the network mode.
Design/methodology/approach
First of all, 24 main measurement sizes of 427 young females are collected to constitute the measurement database. The database is used as background data support of the system. The images are captured to simplify the way of inputting the anthropometry information to the system. Through the 2D feature sizes extracted from body image and the basic dimensions provided by customer input to the system, so that to gain the body sample which is closest to the customer body type through query matching in the database. The detailed anthropometry information of the closest sample is used to describe the customer. The human body measurement database and the technology of body image acquisition are used to extract the feature sizes to achieve obtaining the anthropometry information in a convenient and detailed way.
Findings
Through query matching to the customer in a test, the body sample closest to the customer is gained, and the matching error rate is 0.0132. In the end, some customer samples are input to test the system, in order to verify the effectiveness of system functions. The matching error rates of five body types are gained all less than 0.006. The error is small, and the matching result is ideal.
Research limitations/implications
The size of database established in the paper can be increased constantly in the future to obtain the more accurately matching result.
Practical implications
The research of anthropometry information obtaining system in garment MTM on the network mode is the basis to achieve gaining the anthropometry information in a convenient and detailed way.
Social implications
Applying the established system of human body measurement information acquisition in this paper, it can achieve to obtain the detailed measurement information of customer through a convenient way, combining the method of human body parameter model establishment in the existing research, it can achieve the complete network tailored mode with detailed measurement information acquisition and 3D virtual fitting functions. And it can provide the most convenient experience and the most ideal garment MTM effect to the customer. This mode can be forecast to be an ideal form of garment MTM on the network in the future.
Originality/value
The anthropometry information obtaining system is the important part of garment MTM system on the network mode. It should be applied to the network mode and can obtain the detailed measurements for garment MTM. In this paper, the human body measurement database and the technology of body image acquisition are used in order to extract the feature size to obtain the anthropometry information in a convenient and detailed way.
Details
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Jiaqi Fang, Kun Ma, Yanfang Qiu, Ke Ji, Zhenxiang Chen and Bo Yang
The discrepancy between the content of an article and its title is a key characteristic of fake news. Current methods for detecting fake news often ignore the significant…
Abstract
Purpose
The discrepancy between the content of an article and its title is a key characteristic of fake news. Current methods for detecting fake news often ignore the significant difference in length between the content and its title. In addition, relying solely on textual discrepancies between the title and content to distinguish between real and fake news has proven ineffective. The purpose of this paper is to develop a new approach called semantic enhancement network with content–title discrepancy (SEN–CTD), which enhances the accuracy of fake news detection.
Design/methodology/approach
The SEN–CTD framework is composed of two primary modules: the SEN and the content–title comparison network (CTCN). The SEN is designed to enrich the representation of news titles by integrating external information and position information to capture the context. Meanwhile, the CTCN focuses on assessing the consistency between the content of news articles and their corresponding titles examining both emotional tones and semantic attributes.
Findings
The SEN–CTD model performs well on the GossipCop, PolitiFact and RealNews data sets, achieving accuracies of 80.28%, 86.88% and 84.96%, respectively. These results highlight its effectiveness in accurately detecting fake news across different types of content.
Originality/value
The SEN is specifically designed to improve the representation of extremely short texts, enhancing the depth and accuracy of analyses for brief content. The CTCN is tailored to examine the consistency between news titles and their corresponding content, ensuring a thorough comparative evaluation of both emotional and semantic discrepancies.