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This study, by applying psychological life-space theory, aims to identify the role of banking activities in customers’ well-being.

Following a quantitative approach, a two-stage method of structural equation modelling was used to test the hypotheses. Through a close-ended questionnaire, data were obtained from banks’ customers. A partial least squares regression was used to test both inner and outer models. Sample power software was used to calculate the minimum sample size of the study.

A new variable namely customer well-being stems from banking activities identified which is assumed to be influenced by bank’s image, perceived value and aroused emotions. It was tested that word of mouth and bank’s marketing activities have impacts on the bank’s image. Moreover, by differentiating the images of different entities, it was proposed that comparative images could be considered as moderating variable. Furthermore, the mediating role of perceived value and aroused emotions are documented.

Applying psychological life-space theory, a new concept that is not limited to the financial aspect of well-being presented and tested. Although researchers in different filed of studies applied this theory, this is the first time that a study presents nominal and operational definitions of the role of banks in customers’ well-being. Moreover, for the first time in the bank marketing literature, images of different entities including consumer’s image, image of bank’s employee and image of bank’s competitors were separated and their causal relations were statistically tested.

Closed circuit television (CCTV) imaging is an increasingly used technology and it is now common place for law enforcement to access CCTV footage as an investigative tool to assist in the nomination of a person of interest, or to aid in the prosecution of an offender. The purpose of this paper is to discuss the role of imaging practitioners in the analysis and interpretation of CCTV images within a law enforcement context. It explores and addresses the limitations of CCTV imaging in evidence with a focus on the interpretation of changes in the visual representation of clothing items.

This paper demonstrates the variations observed in four dark toned garments imaged using one CCTV camera with two different recording settings – visible light and near infrared. The device used was installed and operated in a manner comparable to that used in the public domain, the resulting images indicative of those experienced in casework.

The results display a noticeable change to the tonality of each clothing item between the varied recording conditions. These inconsistencies highlight the limitations of layperson analysis and identify the importance of the inclusion of imaging practitioners when interpreting and analysing such images as evidence.

With an abundance of images in the society, layperson interpretation has become common place. Recognising the value of trained imaging practitioners who can assist law enforcement in analysis and interpretation is paramount to ensuring CCTV images as evidence are used appropriately.

Examination of the image projected through photographs used in marketing materials can enable managers to identify opportunities for creating differentiated positioning in the marketplace. However, the impact of image projected through photographs has not received much attention in academic literature. This paper aims to establish a methodology using dynamic indices that allow a systematic comparative evaluation of the image projected through photographs used in marketing materials of lodging properties located in competing destinations. It also provides an empirical examination of the type of image projected through photographs of lodging properties in those destinations.

Analysis was conducted using a content analysis methodology. A total of 17,187 photographs of 5,820 lodging properties located in 15 different tourism destinations over a period of eight years were examined.

Results revealed that the proposed methodology based on dynamic comparative indices is appropriate for determining the projected image of lodging properties. The dynamic indices, which categorise photographs in three main categories: subject (people), activities (actions carried out) and contextualisation (location), allowed a systematic evaluation of the image projected of lodging properties in competing destinations. Results of the empirical analysis indicated important differences in the image projected through photographs among lodging properties located in 15 destinations.

This study provides the industry and destination marketing organisations (DMOs) with a useful and simple tool for comparatively evaluating the image projected by tourism accommodations. The methodology proposed enables managers to identify the current image and the changes in the image of their lodging properties, their destinations and those of their main competitors over the years, thus providing opportunities for development of more effective differentiation and positioning strategies in their respective target markets.

The proposed tool provides a methodology that can be used to categorise and evaluate the image represented on the photographs used in marketing materials to ensure the projection of the intended image. Furthermore, the proposed methodological tool enables managers to monitor the image projected through photographs used in competitors’ marketing materials and identify changes in their image projected over time.

In clinical analysis, medical image segmentation is an important step to study the anatomical structure. This helps to diagnose and classify abnormality in the image. The wide variations in the image modality and limitations in the acquisition process of instruments make this segmentation challenging. This paper aims to propose a semi-automatic model to tackle these challenges and to segment medical images.

The authors propose Legendre polynomial-based active contour to segment region of interest (ROI) from the noisy, low-resolution and inhomogeneous medical images using the soft computing and multi-resolution framework. In the first phase, initial segmentation (i.e. prior clustering) is obtained from low-resolution medical images using fuzzy C-mean (FCM) clustering and noise is suppressed using wavelet energy-based multi-resolution approach. In the second phase, resultant segmentation is obtained using the Legendre polynomial-based level set approach.

The proposed model is tested on different medical images such as x-ray images for brain tumor identification, magnetic resonance imaging (MRI), spine images, blood cells and blood vessels. The rigorous analysis of the model is carried out by calculating the improvement against noise, required processing time and accuracy of the segmentation. The comparative analysis concludes that the proposed model withstands the noise and succeeds to segment any type of medical modality achieving an average accuracy of 99.57%.

The proposed design is an improvement to the Legendre level set (L2S) model. The integration of FCM and wavelet transform in L2S makes model insensitive to noise and intensity inhomogeneity and hence it succeeds to segment ROI from a wide variety of medical images even for the images where L2S failed to segment them.

This study aims to examine why users search for images, how users describe their image needs and what the images are used for by analysing questions obtained from two Chinese social Q&A sites, Zhihu and Baidu Zhidao.

A total of 1,402 image questions were collected from Zhihu and Baidu Zhidao. Both quantitative analysis and qualitative content analysis were performed to identify user image needs and the potential differences on the two social Q&A sites.

Question-asker’s intention varies in different platforms. Zhihu users asked questions mainly aiming at a promotion of subsequent discussion, whereas users of Baidu Zhidao often did so to seek information. Syntactic attributes were not frequently used in both two sites. Zhihu users were more likely to express subjective evaluations on images (concept, emotion, theme and style) in their questions than users of Baidu Zhidao. In contrast, questions from Baidu Zhidao showed a tendency to more frequently include descriptive metadata (rights, format, size, quality and authenticity) and semantic attributes (generic activity, specific people, fashion and text) of the images than questions from Zhihu. Learning was an important use on social Q&A sites, especially on Baidu Zhidao. In addition, the images were primarily used to trigger emotion or served a persuasive purpose in Zhihu.

This study contributes to a better understanding of user image search behaviour, and the findings could be used to develop better image services on social Q&A sites. Meanwhile, the image attributes extracted from the questions are conducive to the improvement of image retrieval systems.

This study explored the features of image needs on social Q&A sites, especially considering image use specified in the question. The difference of image needs between two Chinese social Q&A sites (Zhihu and Baidu Zhidao) was identified.

With the advancements in photo editing software, it is possible to generate fake images, degrading the trust in digital images. Forged images, which appear like authentic images, can be created without leaving any visual clues about the alteration in the image. Image forensic field has introduced several forgery detection techniques, which effectively distinguish fake images from the original ones, to restore the trust in digital images. Among several forgery images, spliced images involving human faces are more unsafe. Hence, there is a need for a forgery detection approach to detect the spliced images.

This paper proposes a Taylor-rider optimization algorithm-based deep convolutional neural network (Taylor-ROA-based DeepCNN) for detecting spliced images. Initially, the human faces in the spliced images are detected using the Viola–Jones algorithm, from which the 3-dimensional (3D) shape of the face is established using landmark-based 3D morphable model (L3DMM), which estimates the light coefficients. Then, the distance measures, such as Bhattacharya, Seuclidean, Euclidean, Hamming, Chebyshev and correlation coefficients are determined from the light coefficients of the faces. These form the feature vector to the proposed Taylor-ROA-based DeepCNN, which determines the spliced images.

Experimental analysis using DSO-1, DSI-1, real dataset and hybrid dataset reveal that the proposed approach acquired the maximal accuracy, true positive rate (TPR) and true negative rate (TNR) of 99%, 98.88% and 96.03%, respectively, for DSO-1 dataset. The proposed method reached the performance improvement of 24.49%, 8.92%, 6.72%, 4.17%, 0.25%, 0.13%, 0.06%, and 0.06% in comparison to the existing methods, such as Kee and Farid's, shape from shading (SFS), random guess, Bo Peng et al., neural network, FOA-SVNN, CNN-based MBK, and Manoj Kumar et al., respectively, in terms of accuracy.

The Taylor-ROA is developed by integrating the Taylor series in rider optimization algorithm (ROA) for optimally tuning the DeepCNN.

The purpose of this paper is to provide a way to analyze satellite images using various clustering algorithms and refined bitplane methods with other supporting techniques to prove the superiority of RIFCM.

A comparative study has been carried out using RIFCM with other related algorithms from their suitability in analysis of satellite images with other supporting techniques which segments the images for further process for the benefit of societal problems. Four images were selected dealing with hills, freshwater, freshwatervally and drought satellite images.

The superiority of the proposed algorithm, RIFCM with refined bitplane towards other clustering techniques with other supporting methods clustering, has been found and as such the comparison, has been made by applying four metrics (Otsu (Max-Min), PSNR and RMSE (40%-60%-Min-Max), histogram analysis (Max-Max), DB index and D index (Max-Min)) and proved that the RIFCM algorithm with refined bitplane yielded robust results with efficient performance, reduction in the metrics and time complexity of depth computation of satellite images for further process of an image.

For better clustering of satellite images like lands, hills, freshwater, freshwatervalley, drought, etc. of satellite images is an achievement.

The existing system extends the novel framework to provide a more explicit way to analyze an image by removing distortions with refined bitplane slicing using the proposed algorithm of rough intuitionistic fuzzy c-means to show the superiority of RIFCM.

Due to the worldwide growth of digital image sharing and the maturity of the tourism industry, the vast and growing collections of digital images have become a challenge for those who use and/or manage these image data across tourism settings. To overcome the image indexing task with less labour cost and improve the image retrieval task with less human errors, the content-based image retrieval (CBIR) technique has been investigated for the tourism domain particularly. This paper aims to review the relevant literature in the field to understand these previous works and identify research gaps for future directions.

A systematic and comprehensive review of CBIR studies in tourism from the year 2010 to 2019, focussing on journal articles and conference proceedings in reputable online databases, is conducted by taking a comparative approach to critically analyse and address the trends of each fundamental element in these research experiments.

Based on the review of the literature, the trends of CBIR studies in tourism is to improve image representation and retrieval by advancing existing feature extraction techniques, contributing novel techniques in the feature extraction process through fine-tuning fusion features and improving image query of CBIR systems. Co-authorship, tourist attraction sector and fusion image features have been in focus. Nonetheless, the number of studies in other tourism sectors and available image databases could be further explored.

The fact that no existing academic review of CBIR studies in tourism makes this paper a novel contribution.

The purpose of this paper is to investigate the relationships among brand experience, sports event image and loyalty in the Jeju International Ultramarathon Race.

Data were collected from 313 ultramarathon runners during the 16th Jeju International Ultramarathon Race. Statistical analyses were carried out using confirmatory factor analysis and structural equation modeling.

An analysis of the relationship between brand experience and sports event image revealed that affective experience is the most influential dimension, followed by sensory and behavioral experiences. However, intellectual experience showed insignificant relationship. Sports event image indicated a positive effect on word-of-mouth and revisit intention.

Ultramarathon races are sporting events with few participants, even though it is becoming more popular. The attitude, motivation and personality of people participating in these races may be different in terms of the extreme characteristic and endurance level of sports activity. If the results of this study need to be generalized with other sports event images, a comparative study may help to understand the personality traits of people participating in ultramarathon races and other sports.

As ultramarathon races are not yet popular sports, to target more people, behavioral experience has an influential role because it is action oriented and actively engages the body. Enriching consumer life by facilitating and creating opportunities to physically experience as well as showing them alternate ways of doing things may attract them to substitute lifestyle and interactions (Pine and Gilmore, 1999). Few other popular endurance race events organize short-distance races and kid races for fun (e.g. Lavaredo Ultra trail race), to engage friends and family members before the start of the main event. Doing so may provide an opportunity to attract more runners, and they may engage in running activities and participate in these kinds of events later, after having this sort of casual experience.

Social networking sites may be used to maintain continuous interaction with the runners and followers. Supporting a social cause is another measure to generate the intellectual experience of sports events. The result of this study shows no significant relationship between intellectual experience and the sports event image. To increase the worldwide popularity of this race and to attract more international runners, a judicious use of web platforms may be an option to maintain regular contact with a wider group of audience.

This study is the first to examine the relationship between brand experience and sports event image in an ultramarathon event context. Findings contribute to an understanding of the impact of brand experience on sports event image, thereby influencing the loyalty of runners participating in an ultramarathon event.

Although tourism destination image is a widely studied subject, the perspective of local players is generally neglected, albeit its relevance for informing the positioning and brand management strategies of the places is recognized. This chapter aims to determine the perceptions of key local public organizations from the historical-cultural and heritage sectors and companies linked to commerce and tourism (private sector) about the historic center of a United Nations Educational, Scientific, and Cultural Organization (UNESCO) World Heritage site in Brazil. The results of this investigation contribute to the development of marketing and tourism development strategies in historic towns, especially those classified as World Heritage by UNESCO.