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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.

We make use of the Volunteered Geographic Information (VGI) data to extract the total extent of the roads using remote sensing images. VGI data is often provided only as vector data represented by lines and not as full extent. Also, high geolocation accuracy is not guaranteed and it is common to observe misalignment with the target road segments by several pixels on the images. In this work, we use the prior information provided by the VGI and extract the full road extent even if there is significant mis-registration between the VGI and the image. The method consists of image segmentation and traversal of multiple agents along available VGI information. First, we perform image segmentation, and then we traverse through the fragmented road segments using autonomous agents to obtain a complete road map in a semi-automatic way once the seed-points are defined. The road center-line in the VGI guides the process and allows us to discover and extract the full extent of the road network based on the image data. The results demonstrate the validity and good performance of the proposed method for road extraction that reflects the actual road width despite the presence of disturbances such as shadows, cars and trees which shows the efficiency of the fusion of the VGI and satellite images.

Despite the enormous benefits, smart homes (SHs) are still not widely adopted by residents in China. Furthermore, research on the intention to use SHs has overlooked the role of family factors. Thus, this study aims to propose a new research model to examine the impact of family factors on the usage intention (UI) of SHs.

This study collected 265 valid data from potential users of SHs in China using a convenience sampling method. The PLS-SEM method was applied to test the research model and related hypotheses.

The empirical results confirm the mediating role of optimism (OP) in perceived family support (PFS)/perceived family trust (PFT) and attitude (AT). Unsurprisingly, the results validated the relationship between perceived usefulness (PU), attitude (AT) and UI of SHs through TAM theory. Besides, this study also identified the moderating effect of perceived risk (PR) between AT and UI.

To improve SH adoption, practitioners should focus on family factors and utilize family influence to promote the spread of smart home reputation. Besides, SH practitioners should enhance user trust and reduce perceived risks through technological upgrades and security measures.

Based on the Social Impact Theory and Technology Acceptance Model (TAM), this study is an empirical attempt to explore the impact of family factors on the intention to use SHs, expanding the research on smart home adoption.

This paper aims to quest the strategies and paths of Chinese automobile firms for being world class. It analyzes their strengths and potentials in comparison with the development experience of the global examples and provides policy recommendations for cultivating world-class automobile firms.

The authors apply the analytic hierarchy process method to evaluate the competitiveness of automobile firms with multiple indicators.

The evaluation results suggest that Chinese automobile firms still lagged behind their world-class peers. Especially, Chinese domestic firms developed unevenly so that they could not make progress in the core parametric dimensions. Nevertheless, Chinese firms could achieve world class, at least in some niche segments, supported by its accumulated technological capacity and tremendous market size.

This research is the first scholarly work to evaluate the competitiveness of Chinese automobile firms and provides insightful comments on its industrial policies in the automobile industry. This may be valuable for policymaking in the automobile sector of China and other developing economies.

As information technology advances, the prevalence of AI chatbot products is on the rise. Despite optimistic market projections, consumer skepticism towards these agents persists. This paper aims to expand the scope of the technology acceptance model by integrating the aspect of appearance. It examines the influence of different attributes of AI chatbot, such as usefulness, ease of use and appearance, individually and in combination, on consumers' intentions to share and purchase.

Using an exploratory study of Web Texts, a 2 (usefulness: high vs low) × 2 (ease of use: high vs low) mixed design and a 2 (usefulness: high vs low) × 2 (ease of use: high vs low) × 2 (anthropomorphism appearance: humanoid vs cartoon) for between-subjects designs and the price level (high vs low) for within-subjects designs. The hypotheses were tested by Octoparse and SPSS 22.0.

The research highlights the significant role of usefulness, ease of use and anthropomorphic appearance in shaping consumer attitudes towards AI chatbots, thus influencing their intentions to share information and make purchases. Grouped regression analysis reveals that lower prices exert a more pronounced positive influence on consumers' inclinations to both share and purchase, compared to higher prices. Moreover, novelty-seeking behavior moderates the effect of perceived usefulness or ease of use on attitude. Specifically, heightened novelty-seeking tendencies mitigate the impact of low perceived usefulness or ease of use, leading to sustained positive attitudes towards AI chatbots among consumers.

This study innovatively incorporates product appearance into the Technology Acceptance Model (TAM), considering both the functional attributes and appearance of AI chatbot and their impact on consumers. It offers valuable insights for marketing strategies, extends the scope of TAM application and holds significant practical implications for enhancing enterprise product planning.

随着信息技术的进步, AI聊天机器人产品的普及正在增长。尽管市场对这些代理人持乐观态度, 但消费者对这些代理人的怀疑仍然存在。本文旨在通过整合外观方面来扩展技术接受模型的范围。它考察了AI聊天机器人的不同属性（如有用性、易用性和外观）对消费者分享和购买意图的影响, 单独以及组合。

使用Web文本的探索性研究, 一个2（有用性：高vs低）× 2（易用性：高vs低）的混合设计和一个2（有用性：高vs低）× 2（易用性：高vs低）× 2（人格化外观：类人形vs卡通）用于受试者间设计和价格水平（高vs低）用于受试者内设计。通过 Octoparse 和 SPSS 22.0 测试假设。

研究突出了有用性、易用性和拟人化外观在塑造消费者对AI聊天机器人态度方面的重要作用, 从而影响了他们分享信息和购买的意图。分组回归分析显示, 相对于高价格, 低价格对消费者分享和购买的倾向产生了更为显著的正面影响。此外, 新奇寻求行为调节了感知有用性或易用性对态度的影响。具体来说, 增强的新奇寻求倾向减轻了对低感知有用性或易用性的影响, 导致消费者对AI 聊天机器人持续保持积极态度。

本研究将产品外观创新地纳入技术接受模型（TAM）中, 考虑了AI 聊天机器人的功能属性和外观以及它们对消费者的影响。它为营销策略提供了有价值的见解, 拓展了TAM的应用范围, 并对增强企业产品规划产生了重要的实际影响。