An efficient image retrieval scheme for colour enhancement of embedded and distributed surveillance images

From the past few years, the size of the data grows exponentially with respect to volume, velocity, and dimensionality due to wide spread use of embedded and distributed surveillance cameras for security reasons. In this paper, we have proposed an integrated approach for biometric-based image retrieval and processing which addresses the two issues. The first issue is related to the poor visibility of the images produced by the embedded and distributed surveillance cameras, and the second issue is concerned with the effective image retrieval based on the user query. This paper addresses the first issue by proposing an integrated image enhancement approach based on contrast enhancement and colour balancing methods. The contrast enhancement method is used to improve the contrast, while the colour balancing method helps to achieve a balanced colour. Importantly, in the colour balancing method, a new process for colour cast adjustment is introduced which relies on statistical calculation. It adjusts the colour cast and maintains the luminance of the image. The integrated image enhancement approach is applied to the enhancement of low quality images produced by surveillance cameras. The paper addresses the second issue relating to image retrieval by proposing a content-based image retrieval approach. The approach is based on the three features extraction methods namely colour, texture and shape. Colour histogram is used to extract the colour features of an image. Gabor filter is used to extract the texture features and the moment invariant is used to extract the shape features of an image. The use of these three algorithms ensures that the proposed image retrieval approach produces results which are highly relevant to the content of an image query, by taking into account the three distinct features of the image and the similarity metrics based on Euclidean measure. In order to retrieve the most relevant images, the proposed approach also employs a set of fuzzy heuristics to improve the quality of the results further. The result

An Investigation into the Performance of Ethnicity Verification Between Humans and Machine Learning Algorithms

There has been a significant increase in the interest for the task of classifying demographic profiles i.e. race and ethnicity. Ethnicity is a significant human characteristic and applying facial image data for the discrimination of ethnicity is integral to face-related biometric systems. Given the diversity in the application of ethnicity-specific information such as face recognition and iris recognition, and the availability of image datasets for more commonly available human populations, i.e. Caucasian, African-American, Asians, and South-Asian Indians. A gap has been identified for the development of a system which analyses the full-face and its individual feature-components (eyes, nose and mouth), for the Pakistani ethnic group. An efficient system is proposed for the verification of the Pakistani ethnicity, which incorporates a two-tier (computer vs human) approach. Firstly, hand-crafted features were used to ascertain the descriptive nature of a frontal-image and facial profile, for the Pakistani ethnicity. A total of 26 facial landmarks were selected (16 frontal and 10 for the profile) and by incorporating 2 models for redundant information removal, and a linear classifier for the binary task. The experimental results concluded that the facial profile image of a Pakistani face is distinct amongst other ethnicities. However, the methodology consisted of limitations for example, low performance accuracy, the laborious nature of manual data i.e. facial landmark, annotation, and the small facial image dataset. To make the system more accurate and robust, Deep Learning models are employed for ethnicity classification. Various state-of-the-art Deep models are trained on a range of facial image conditions, i.e. full face and partial-face images, plus standalone feature components such as the nose and mouth. Since ethnicity is pertinent to the research, a novel facial image database entitled Pakistani Face Database (PFDB), was created using a criterion-specific selection process, to ensure assurance in each of the assigned class-memberships, i.e. Pakistani and Non-Pakistani. Comparative analysis between 6 Deep Learning models was carried out on augmented image datasets, and the analysis demonstrates that Deep Learning yields better performance accuracy compared to low-level features. The human phase of the ethnicity classification framework tested the discrimination ability of novice Pakistani and Non-Pakistani participants, using a computerised ethnicity task. The results suggest that humans are better at discriminating between Pakistani and Non-Pakistani full face images, relative to individual face-feature components (eyes, nose, mouth), struggling the most with the nose, when making judgements of ethnicity. To understand the effects of display conditions on ethnicity discrimination accuracy, two conditions were tested; (i) Two-Alternative Forced Choice (2-AFC) and (ii) Single image procedure. The results concluded that participants perform significantly better in trials where the target (Pakistani) image is shown alongside a distractor (Non-Pakistani) image. To conclude the proposed framework, directions for future study are suggested to advance the current understanding of image based ethnicity verification.Acumé Forensi

Media Transformations: Framing, Multimodality and Visual Literacy in Contemporary Media Spaces

Multimodal theory has developed out of social semiotics and can be seen as a response to the rise in the use of new technologies for the creation, distribution and consumption of media texts and the need to find new ways of describing and explaining their role in representation and communication. Its development is historical. It is a response to change over time. The incorporation of the visual into social semiotics marks a key moment in the development of multimodal theory. Visual literacy is discussed in relation to changes in modes of representation and a critique of this concept is provided. This is conducted in relation to how the visual modality has been integrated into social semiotics as a platform for research into multimodal communication more generally. Framing is developed along three main lines of enquiry (semiotic, cognitive and affective) as alternative ways of accounting for some of these shifts in communication and each are presented in the form of case studies. Framing and its close relationship with composition in media texts is discussed and this understanding, one that emphasise proximity as a multimodal principle, is applied to the visual design of content, the realisation of context through the provision visual cues, and later to embodiment and urban space. The three case studies, the application of framing to a range of media texts, the critical judgements made about the role visual in contemporary theory and the application of these concepts to multimodality are presented as part of an intellectual journey

Investigating the Experiences of Lecturers Using Mobile Technology to Teach English at Saudi Universities.

Mobile learning as a support to teaching English as a Foreign Language (EFL) is still in the early adoption stage worldwide, and in Saudi universities in particular. Such adoption requires several elements to be considered, including the readiness and acceptance towards adopting mobile learning among instructors, which is a critical aspect of ensuring successful implementation. Therefore, this study investigates lecturers’ perceptions and use of mobile learning in teaching EFL, using the Unified Theory of Acceptance and Use of Technology (UTAUT2) to guide the research and illuminate the factors that affect the acceptance of mobile learning in the Saudi context. This study followed a mixed-method sequential explanatory approach, with data collected through a questionnaire survey (n=270) and semi-structured interviews (n=12). The quantitative data were analysed using SPSS, which included both descriptive and inferential statistics, with the qualitative data from the semi-structured interviews analysed via thematic analysis. The regression and moderation analyses revealed that habit and hedonic motivation have the most significant impact on the behavioural intention of the lecturers to use mobile technology in teaching practice, followed by performance expectancy and effort expectancy. Secondly, facilitating conditions have the most significant influence on the use behaviour to use mobile technology, followed by habit and price value. The education level of the lecturers moderated the relationship between effort expectancy and behavioural intention to use mobile technologies, with the effect increasing as the level of education decreased. Age also moderated the relationship between effort expectancy and the use behaviour to use mobile technologies, where the effect increased with age, as per the relationship between social influence and the behavioural intention to use mobile technologies. Age and education also moderated the relationship between facilitating conditions and the behavioural intention to use mobile technologies, with the effect increasing as the education level decreased and the age increased. Furthermore, gender moderated the relationship between facilitating conditions and the use behaviour to use mobile technologies, where the impact was greater among females than males. Experience also moderated the relationship between price value and use behaviour, with the effect increasing as the level of experience decreased. This study presents recommendations to those responsible for implementing mobile learning in Saudi universities, such as government decision-makers and university leaders, which relate to the type of training needed, concerns regarding university policy, mobile learning strategy, and overcoming culture and privacy, particularly for female instructors. The study is expected to be submitted to the Saudi Ministry of Education in 2020 to support its review of the Vision 2030 initiative