Search results

The purpose of this study aims to identify the impact of verbal-visual cognitive styles on the level of satisfaction and behavior in the textual and content search of Google Images.

“Riding” cognitive style test and satisfaction questionnaire were used as data collection tools. Also, to collect data related to the image search behavior, the subjects’ transaction files were recorded using Camtasia software and then the files observed and reviewed. The research sample was 90 postgraduate students of Shiraz University.

The results showed that cognitive styles in interaction with the text-based and content-based search system of “Google Images” affected user’s satisfaction. Text-based image retrieval, in which vocabulary-based information needs were expressed, was more compatible with the verbal cognitive style and resulted in greater satisfaction. In contrast, in content-based image retrieval, where it was possible to express information needs in the form of images, users were more satisfied with the visual cognitive style. Verbal users performed more positively in text-based search and visual users in content-based search.

Considering the research gap, which has identified the performance of visual text-based and content-based systems in terms of satisfaction and cognitive style search behavior, the present study could be considered a small effort to promote science.

The main obstacle in realising semantic‐based image retrieval from the web is that it is difficult to capture semantic description of an image in low‐level features. Text‐based keywords can be generated from web documents to capture semantic information for narrowing down the search space. The combination of keywords and various low‐level features effectively increases the retrieval precision. The purpose of this paper is to propose a dynamic approach for integrating keywords and low‐level features to take advantage of their complementary strengths.

Image semantics are described using both low‐level features and keywords. The keywords are constructed from the text located in the vicinity of images embedded in HTML documents. Various low‐level features such as colour histograms, texture and composite colour‐texture features are extracted for supplementing keywords.

The retrieval performance is better than that of various recently proposed techniques. The experimental results show that the integrated approach has better retrieval performance than both the text‐based and the content‐based techniques.

The features of images used for capturing the semantics may not always describe the content.

The indexing mechanism for dynamically growing features is challenging while practically implementing the system.

A survey of image retrieval systems for searching images available on the internet found that no internet search engine can handle both low‐level features and keywords as queries for retrieving images from WWW so this is the first of its kind.

This study presents an innovative approach to analyzing user behavior when performing digital tasks by integrating eye-tracking technology. Through the measurement of user scan patterns, gaze and attention during task completion, the authors gain valuable insights into users' approaches and execution of these tasks.

In this research, the authors conducted an observational study that centered on assessing the digital skills of individuals with limited proficiency who enrolled in a computer introductory course. A group of 19 participants were tasked with completing various online assignments both before and after completing the course.

The study findings indicate a significant improvement in participants' skills, particularly in basic and straightforward applications. However, advancements in more sophisticated utilization, such as mastering efficient search techniques or harnessing the Internet for enhanced situational awareness, demonstrate only marginal enhancement.

In recent decades, extensive research has been conducted on the issue of digital inequality, given its significant societal implications. This paper introduces a novel tool designed to analyze digital inequalities and subsequently employs it to evaluate the effectiveness of “LEHAVA,” the largest government-sponsored program aimed at mitigating these disparities in Israel.

This study aims to evaluate a new fusion technique of visual and textual clusters of objects from a real multimedia data-driven collection to improve the performance of multimedia applications.

The authors focused on using multi-criteria for clustering texts and images. The algorithm consists of these steps: first is text representation using the statistical method of weighting, second is image representation using a bag of words feature descriptors methods and finally application of multi-criteria clustering.

As an application for event detection based on social multimedia data, in particular, Flickr platform. Several experiments were conducted to choose the appropriate parameters for a better scheme of clustering. The new approach achieves better performance when aggregate text clustering is done with image clustering for event detection.

Further researches would be investigated on other social media platforms such as Facebook and Twitter for a generalization of the technique.

This study contributes to multimedia data mining through the new fusion technique of clustering. The technique has its root in such strong field as the field of multi-criteria clustering and decision-making support.

Spatial identity is an important constituent of general cultural identity in that it provides its share of continuity, sustainability, character and inertia. The purpose of this paper is to trace spatial identity’s formulation, reflection and perception within the mainstream media. The authors are particularly interested in spatial identity’s general aspect, consisting of architectural and other elements that give spatial character to places, making them both common and recognisable at the same time. The proposed spatial identity presence index is one of the indicators through which stakeholders in cultural heritage management could monitor, and even manage, the public perception of built heritage’s wider context.

The research seeks wider relevance through the development of new methodology that combines web search services, visual data quantification, and data mining methods, and compares this with expert opinion. The research methodology is showcased and established in terms of the connection between the fundamental work in relation to Slovenian architectural landscapes from the pre-internet era and spatial identity’s web reflection as broadcast and collectively co-shaped by the internet-permeated society more than 20 years after the internet’s inception.

The findings indicate that results based on expert opinion and results acquired by counting spatial character carrier elements are aligned.

The introduced index of web-sourced spatial identity presence measures web-projected spatial characteristics in selected settlements. It is applicable in similar cases where the existing body of work on local spatial identity allows it, and can be used for comparative purposes. It also has social, economic and political connotations.

Research suggests that memory for unfamiliar faces is pictorial in nature, with recognition negatively affected by changes to image-specific information such as head pose, lighting and facial expression. Further, within-person variation causes some images to resemble a subject more than others. Here, the purpose of this paper is to explore the impact of target-image choice on face construction using a modern evolving type of composite system, EvoFIT.

Participants saw an unfamiliar target identity and then created a single composite of it the following day with EvoFIT by repeatedly selecting from arrays of faces with “breeding”, to “evolve” a face. Targets were images that had been previously categorised as low, medium or high likeness, or a face prototype comprising averaged photographs of the same individual.

Identification of composites of low likeness targets was inferior but increased as a significant linear trend from low to medium to high likeness. Also, identification scores decreased when targets changed by pose and expression, but not by lighting. Similarly, composite identification from prototypes was more accurate than those from low likeness targets, providing some support that image averages generally produce more robust memory traces.

The results emphasise the potential importance of matching a target's pose and expression at face construction; also, for obtaining image-specific information for construction of facial-composite images, a result that would appear to be useful to developers and researchers of composite software.

This current project is the first of its kind to formally explore the potential impact of pictorial properties of a target face on identifiability of faces created from memory. The design followed forensic practices as far as is practicable, to allow good generalisation of results.

Public service announcements (PSAs) are frequently used tools to try to change attitudes and behaviors on social issues, including texting and driving, which has been social problem for over a decade. However, the effectiveness of such PSA campaigns often meet with varying degrees of success, suggesting changes to current anti-texting and driving campaigns are needed. This study aims to examine how to design more effective anti-texting and driving PSA campaigns by identifying the elements of existing campaigns that have the strongest impact on attitude change.

In total, 682 respondents from Amazon’s Mechanical Turk participated in an online study in which they evaluated 162 real-world anti-texting and driving ads. Respondents evaluated the ads on various ad elements (i.e. type of appeal, source of emotion, discrete emotions and perceived creativity), as well as their attitudes toward the issue after seeing the ad.

PSAs that use emotional (vs rational) appeals, evoke emotion through imagery (vs text) and/or use fear (vs disgust, anger or guilt) result in the largest changes in attitude. In addition, more creative PSAs are more effective at changing attitudes.

Overall, the results provide useful information to social marketers on how to design more effective anti-texting and driving campaigns.

This paper aims to offer both a practical and reflective stance on a longitudinal multi-method interpretive consumer research project carried out with tween girls.

This multi-layered approach to data collection, involving qualitative diaries, accompanied shopping trips, e-collages and in-depth interviews, addresses the need, as articulated by Morrow and Richards (1996, p. 96) to “move away from the narrow focus of socialization and child development” toward a research approach that prioritizes children’s own experiences of their lives as children, thereby reconsidering the richness of children’s voices.

In line with those whose work seeks to privilege children’s knowledge of the world they inhabit while also emphasizing the need, as in the case of adult “doing” to place that existence within its broader social context (Russell and Tyler, 2005, p. 227), diaries, in-depth interviews, shopping trips, e-collages and researcher diaries were used to access the world of these social neophytes as they mediate their social worlds through the ever pervasive prism of consumer culture. The light and shade of their worlds cannot be captured by adult-oriented perspectives on research which assume that young consumers are incompetent, worthy of debate merely to ascertain levelness of agency or of interest merely to quantify degrees of participation in and comprehension of the semiotic markers of our consumer society.

Only female consumers were involved in this study which underlines the need to engage with both genders when it comes to researching young consumers.

This paper offers a tangible contribution to the movement of research toward understanding young consumers’ worlds through engagement with multi-layered discourses and representations.

This multi-layered, multi-method research project acknowledges the enthralling complexity of these young consumers’ social worlds, giving a richness and immediacy to their accounts of the compelling intimacy between young adolescent identity and the marketplace.

The purpose of this study is to explore the retrieval effectiveness of three image search engines (ISE) – Google Images, Yahoo Image Search and Picsearch in terms of their image retrieval capability. It is an effort to carry out a Cranfield experiment to know how efficient the commercial giants in the image search are and how efficient an image specific search engine is.

The keyword search feature of three ISEs – Google images, Yahoo Image Search and Picsearch – was exploited to make search with keyword captions of photos as query terms. Selected top ten images were used to act as a testbed for the study, as images were searched in accordance with features of the test bed. Features to be looked for included size (1200 × 800), format of images (JPEG/JPG) and the rank of the original image retrieved by ISEs under study. To gauge the overall retrieval effectiveness in terms of set standards, only first 50 result hits were checked. Retrieval efficiency of select ISEs were examined with respect to their precision and relative recall.

Yahoo Image Search outscores Google Images and Picsearch both in terms of precision and relative recall. Regarding other criteria – image size, image format and image rank in search results, Google Images is ahead of others.

The study only takes into consideration basic image search feature, i.e. text-based search.

The study implies that image search engines should focus on relevant descriptions. The study evaluated text-based image retrieval facilities and thereby offers a choice to users to select best among the available ISEs for their use.

The study provides an insight into the effectiveness of the three ISEs. The study is one of the few studies to gauge retrieval effectiveness of ISEs. Study also produced key findings that are important for all ISE users and researchers and the Web image search industry. Findings of the study will also prove useful for search engine companies to improve their services.

The purpose of this paper is to investigate image search and retrieval problems in selected search engines in relation to Persian writing style challenges.

This study is an applied one, and to answer the questions the authors used an evaluative research method. The aim of the research is to explore the morphological and semantic problems of Persian language in connection with image search and retrieval among the three major and widespread search engines: Google, Yahoo and Bing. In order to collect the data, a checklist designed by the researcher was used and then the data were analyzed by descriptive and inferential statistics.

The results indicate that Google, Yahoo and Bing search engines do not pay enough attention to morphological and semantic features of Persian language in image search and retrieval. This research reveals that six groups of Persian language features include derived words, derived/compound words, Persian and Arabic Plural words, use of dotted T and the use of spoken language and polysemy, which are the major problems in this area. In addition, the results suggest that Google is the best search engine of all in terms of compatibility with Persian language features.

This study investigated some new aspects of the above-mentioned subject through combining morphological and semantic aspects of Persian language with image search and retrieval. Therefore, this study is an interdisciplinary research, the results of which would help both to offer some solutions and to carry out similar research on this subject area. This study will also fill a gap in research studies conducted so far in this area in Farsi language, especially in image search and retrieval. Moreover, findings of this study can help to bridge the gap between the user’s questions and search engines (systems) retrievals. In addition, the methodology of this paper provides a framework for further research on image search and retrieval in databases and search engines.