Search results

Image Processing is an emerging field that is used to extract information from images. In recent years, this field has received immense attention from researchers, especially in the research domains of object detection, Biomedical Imaging and Semantic segmentation. In this study, a bibliometric analysis of publications related to image processing in the Science Expanded Index Extended (SCI-Expanded) has been performed. Several parameters have been analyzed such as annual scientific production, citations per article, most cited documents, top 20 articles, most relevant authors, authors evaluation using y-index, top and most relevant sources (journals) and hot topics.

The Bibliographic data has been extracted from the Web of Science which is well known and the world's top database of bibliographic citations of multidisciplinary areas that covers the various journals of computer science, engineering, medical and social sciences.

The research work in image processing is meager in the past decade, however, from 2014 to 2019, it increases dramatically. Recently, the IEEE Access journal is the most relevant source with an average of 115 publications per year. The USA is most productive and its publications are highly cited while China comes in second place. Image Segmentation, Feature Extraction and Medical Image Processing are hot topics in recent years. The National Natural Science Foundation of China provides 8% of all funds for Image Processing. As Image Processing is now becoming one of the most critical fields, the research productivity has enhanced during the past five years and more work is done while the era of 2005–2013 was the area with the least amount of work in this area.

This research is novel in this regard that no previous research focuses on Bibliometric Analysis in the Image Processing domain, which is one of the hot research areas in computer science and engineering.

The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned from single modalities, such as text, images, audio and video, to integrated multimodal forms. This paper identifies key trends, gaps and areas of focus in the field. Furthermore, it proposes a theoretical organizational framework based on deep learning to address the challenges of managing archives in the era of big data.

Via a comprehensive systematic literature review, the authors investigate the field of multimodal archive resource organization and the application of deep learning techniques in archive organization. A systematic search and filtering process is conducted to identify relevant articles, which are then summarized, discussed and analyzed to provide a comprehensive understanding of existing literature.

The authors' findings reveal that most research on multimodal archive resources predominantly focuses on aspects related to storage, management and retrieval. Furthermore, the utilization of deep learning techniques in image archive retrieval is increasing, highlighting their potential for enhancing image archive organization practices; however, practical research and implementation remain scarce. The review also underscores gaps in the literature, emphasizing the need for more practical case studies and the application of theoretical concepts in real-world scenarios. In response to these insights, the authors' study proposes an innovative deep learning-based organizational framework. This proposed framework is designed to navigate the complexities inherent in managing multimodal archive resources, representing a significant stride toward more efficient and effective archival practices.

This study comprehensively reviews the existing literature on multimodal archive resources organization. Additionally, a theoretical organizational framework based on deep learning is proposed, offering a novel perspective and solution for further advancements in the field. These insights contribute theoretically and practically, providing valuable knowledge for researchers, practitioners and archivists involved in organizing multimodal archive resources.

Understanding the multifaceted images of tourism destinations is critical for effective destination marketing and management strategies. Traditional approaches, including conceptualization of destination images or analysis of their antecedents and consequences, are commonly used. This study aims to advocate the inclusion of visitors’ latent profiles based on cognitive images to enrich the evaluation and formulation of destination marketing and management strategies.

The analysis focuses on Serbia, an emerging destination, that attracts an increasing number of first-time, repeat and prospective visitors. Exploratory factor analysis and confirmatory factor analysis were used to test the potential dimensions (tangible and intangible cultural destination; infrastructural and accessible destination; active, nature and family destination; sensory and hospitable destination; and welcoming, value for money (VFM) and safe destination) of the cognitive destination image factors scale while subtypes (profiles) were obtained using latent profile analysis (LPA).

The cognitive image component encompasses the perceived attributes of a destination, whether derived from direct experience or acquired through other means. The study identified the following profiles: conventional destination; sensory and hospitable destination; welcoming, VFM and safe destination; secure and active family destination and accessible cultural destination, which are presented individually with their sociodemographic assets.

The main contribution of the paper is the application of a novel method (LPA) for profiling visitor segments based on cognitive destination image. From a theoretical perspective, this research contributes to the extant body of literature pertaining to the destination image, thereby facilitating the identification of discrete latent visitor segments and elucidating noteworthy differences among them concerning a cognitive image.

This study aims to examine how veganism is “seen” by young adult non-vegan consumers and how prevailing attitudes reinforce or challenge stigmas around veganism.

Photovoice methodology was used to explore young non-vegan consumers’ attitudes and beliefs towards veganism. Data was collected from students studying advertising at a major university in Australia, who produced images and narratives reflective of their own attitudes towards veganism. Polytextual thematic analysis of the resulting visual data was then undertaken to reveal the dominant themes underpinning participants’ attitudes. Participant narratives were then reviewed to confirm whether the ascribed meaning aligned with participants’ intended meaning.

Participant images were reflective of first, how they saw their world and their place within it, which showed the interplay and interconnectedness between humans, animals and nature, and second, how they saw vegans within this world, with both positive and negative attitudes expressed. Interestingly, vegans were simultaneously admired and condemned. By situating these attitudes along a spectrum of moral evaluation, bounded by stigmatisation and moral legitimacy, participants saw vegans as being either Radicals, Pretenders, Virtuous or Pragmatists. For veganism to become more widely accepted by non-vegans, there is an important role to be played by each vegan type.

This study offers a more nuanced understanding of how and why dissociative groups, such as vegans, become stigmatised, which has implications for messaging and marketing practices around veganism and associated products/services. Future research could use a similar methodology to understand why other minority groups in society are stereotyped and stigmatised, which has broader social implications.

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

This study aims to explore visual artists’ image needs and the obstacles they face in meeting them.

The visual artists, participating in the study, included painters, graphic designers, textile designers, architects and sculptors who were faculty members in two oldest art institutions of Pakistan. A total of 20 face-to-face interviews representing four participants from each visual artists group were conducted. The textual data were analyzed thematically, using NVIVO 12 software.

Results showed that under-study visual artists need images mainly for academic purposes (teaching, assignments, etc.) and for professional and research purposes. However, they require images quite often, as a majority of the respondents told that they need images on daily basis.

The study findings provide an insight for information science professionals, system designers and image librarians regarding visual artists’ image using behavior.

As the researchers could not find any such study in local context, and a very few globally, therefore, this study may serve as a baseline for further research in this area.

Tourists with different cultural backgrounds think and behave differently. Accurately capturing and correctly understanding cultural differences will help tourist destinations in product/service planning, marketing communication and attracting and retaining tourists. This research employs Hofstede's cultural dimensions theory to analyse the variations in destination image perceptions of Chinese-speaking and English-speaking tourists to Xiamen, a prominent tourist attraction in China.

The evaluation utilizes a two-stage approach, incorporating LDA and BERT-BILSTM models. By leveraging text mining, sentiment analysis and t-tests, this research investigates the variations in tourists' perceptions of Xiamen across different cultures.

The results reveal that cultural disparities significantly impact tourists' perceived image of Xiamen, particularly regarding their preferences for renowned tourist destinations and the factors influencing their travel experience.

This research pioneers applying natural language processing methods and machine learning techniques to affirm the substantial differences in the perceptions of tourist destinations among Chinese-speaking and English-speaking tourists based on Hofstede's cultural theory. The findings furnish theoretical insights for destination marketing organizations to target diverse cultural tourists through precise marketing strategies and illuminate the practical application of Hofstede's cultural theory in tourism and hospitality.

Sectoral brands are umbrella brands created to represent all companies’ products belonging to a country’s economic industry abroad to enhance their export performance. This study aims to explore the development of a sectoral brand model through the optic of the social constructionist perspective. Besides, this study also proposes to apply the model to a sectoral brand case in the business-to-business market.

The authors have developed a systematic qualitative literature review to provide a theoretical basis for the attributes chosen to compose the social constructionist sectoral brand management (SCSBM) model. To apply the model, the authors have conducted a series of 17 in-depth semi-structured interviews with the association’s managers that constitute the sectoral brand development, the director of the branding consultancy firm and specialists on place branding.

The authors present the SCSBM model, highlighting that sectoral branding should be seen as a dynamic and continuous process with the integrated participation of all industry stakeholders. Moreover, the authors have applied the model to the Brazil Fashion System brand.

The main contribution to theory is the link between sectoral brand management and the social constructionist approach, being the first study, to the best of the authors’ knowledge, to propose this connection. SCSBM model extends previous work on sectoral brands by adopting a social constructionist view.

The SCSBM model might contribute to marketing professionals willing to develop sectoral brands across multiple economic sectors and geographies.

The study’s originality lies in developing the first model, which adopts a social constructionist approach to sectoral brands.

The present study aims to investigate the psychometric properties of the picture story exercise (PSE), a tool for measuring implicit motivations in Iran.

The research method is descriptive correlation and was conducted in two studies. In the first study 24 psychology master’s students were selected by convenience sampling method and tested them to check retest and inter-coder reliability. The second study investigated the correlation between self-reports and the PSE test on a sample of managers. Its statistical population comprised all the managers of a refinery company, 50 people were selected by convenience sampling method. To check the concurrent validity of the PSE test, the participants of the second study, completing the PSE, also completed the needs questionnaire of Steers and Porter (1979).

The findings showed that the PSE test in the Iranian sample had high retest reliability (0.62 on mean) and inter-coder reliability (0.87 on mean), and, following previous research, it does not show a significant relationship with self-reported motives.

PSE can be used in future research as a tool that has demonstrated its reliability and validity in the Iranian sample.

Measurement of implicit motives is a practical factor for predicting people’s behavior, the necessity of using tools that can accurately evaluate implicit motives is strongly felt. Taking into account the fact that so far, in Iranian samples, implicit motivations have not been measured, therefore, the current research is trying to answer this question, whether one of the most important and prominent tools that were made for this purpose and used in various studies can also be used in Iranian samples.

Future is rarely explicitly addressed or problematized in business network research. This study aims to examine the possibilities of developing a business actor’s future orientation to network studies and imports ideas and concepts from futures research to support the development.

The study is conceptual and interdisciplinary. The authors critically analyze how extant studies grounded in the sensemaking view and process research approach integrate future time and how theoretical myopia hinders the adoption of a future orientation.

The prevailing future perspective is restricted to managers’ perceptions and actions at present, ignoring the anticipation and exploration of alternative longer-term futures. Future time is generally conceived as embedded in managers’ cognitive processes or is seen as part of the ongoing interaction, where the time horizon to the future is not noticed or is at best short.

To enable a forward-looking perspective, researchers should move the focus from expectation building in business interaction to purposeful preparation of alternative future(s) and from the view of seeing future as enacted in the present to envisioning of both near-term and more distant futures.

This study addresses the growing need of business actors to anticipate future developments in the rapidly changing market conditions and to innovate and change business practices to save the planet for future generations.

This study elaborates on actors’ future orientation to business markets and networks, proposes the integration of network research concepts with concepts from futures studies and poses new types of research questions for future research.