Using Text Surrounding Method to Enhance Retrieval of Online Images by Google Search Engine

Purpose: the current research aimed to compare the effectiveness of various tags and codes for retrieving images from the Google. Design/methodology: selected images with different characteristics in a registered domain were carefully studied. The exception was that special conceptual features have been apportioned for each group of images separately. In this regard, each image group surrounding texts was dissimilar. Images were allocated with captionsincluding language in Farsi and English, alt text, image title, file name, free and controlled languages and appropriation text to images properties. Findings: allocating texts to images on a website causes Google to retrieve more images. Chi-square test for identification of significant differences among retrieved images in 5 Codes and revealed that in different codes, various numbers of images that were retrieved were significantly different. Caption allocation in English proved to have the best effect in retrieving images in the study sample, whereas file name had less effect in image retrieval ranking. Results of the Kruskal-Wallis test to assess the group differences in 5 codes revealed that differences were significant. Originality/Value: This paper tries to recall the importance of some elements which a search engine like Google may consider in indexing and retrieval of images. Widespread use of image tagging on the web enables Google and also other search engines to successfully retrieve images

Digital libraries: The challenge of integrating instagram with a taxonomy for content management

Interoperability and social implication are two current challenges in the digital library (DL) context. To resolve the problem of interoperability, our work aims to find a relationship between the main metadata schemas. In particular, we want to formalize knowledge through the creation of a metadata taxonomy built with the analysis and the integration of existing schemas associated with DLs. We developed a method to integrate and combine Instagram metadata and hashtags. The final result is a taxonomy, which provides innovative metadata with respect to the classification of resources, as images of Instagram and the user-generated content, that play a primary role in the context of modern DLs. The possibility of Instagram to localize the photos inserted by users allows us to interpret the most relevant and interesting informative content for a specific user type and in a specific location and to improve access, visibility and searching of library content

Analysis of the content of nature scientific photographs: proposal of a pattern

Se propone una plantilla para el análisis de contenido de fotografías científicas de la naturaleza. En primer lugar, se define la fotografía científica, su público y los condicionantes de su análisis. En segundo lugar, se destaca la importancia del texto acompañando las fotografías para su recuperación. En tercer lugar, se describen los modelos de Lasswell, de Panofsky y Shatford y la semiología para el análisis de las fotografías, antes de detallar el método seguido para el análisis de contenido. Por último, se presenta la funcionalidad de la plantilla para la redacción de un resumen y asignación de descriptores. Se concluye destacando su interés para encontrar las pistas informativas transmitidas por las fotografías y la necesaria colaboración entre fotógrafo y documentalista.This article proposes a pattern to analyse the content of scientific photographs. In a first part, it defines scientific photographs, its public and its analysis. In a second part, it deals with the importance of the text which encloses the photographs to be retrieved. In a third part, it describes the Lasswell model, the Panofsky and Shatford models, and the semiology. The method followed to analyse photographs is then detailed. In a fourth part, it stresses how useful is the pattern statistically to write an abstract and to assign keywords, before it concludes about the importance to detect the informative clues transmitted by the photographer and the necessary collaboration between photographer and information professional

A Systematic Literature Review on Image Information Needs and Behaviors

Purpose: With ready access to search engines and social media platforms, the way people find image information has evolved and diversified in the past two decades. The purpose of this paper is to provide an overview of the literature on image information needs and behaviors. Design/methodology/approach: Following an eight-step procedure for conducting systematic literature reviews, the paper presents an analysis of peer-reviewed work on image information needs and behaviors, with publications ranging from the years 1997 to 2019. Findings: Application of the inclusion criteria led to 69 peer-reviewed works. These works were synthesized according to the following categories: research methods, users targeted, image types, identified needs, search behaviors, and search obstacles. The reviewed studies show that people seek and use images for multiple reasons, including entertainment, illustration, aesthetic appreciation, knowledge construction, engagement, inspiration, and social interactions. The reviewed studies also report that common strategies for image searches include keyword searches with short queries, browsing, specialization, and reformulation. Observed trends suggest common deployment of query analysis, survey questionnaires, and undergraduate participant pools to research image information needs and behavior. Originality: At this point, after more than two decades of image information needs research, a holistic systematic review of the literature was long overdue. The way users find image information has evolved and diversified due to technological developments in image retrieval. By synthesizing this burgeoning field into specific foci, this systematic literature review provides a foundation for future empirical investigation. With this foundation set, the paper then pinpoints key research gaps to investigate, particularly the influence of user expertise, a need for more diverse population samples, a dearth of qualitative data, new search features, and information and visual literacies instruction

Race as access: Designation of race through user-assigned tags for digitized archival images

This paper examines how users and describers of digitized archival images perceive race and designate race as access points for retrieval from digital collections. The paper includes an overview of literature discussing race, representation, and bias in controlled vocabularies. Creation of classificatory space, integration of Critical Race Theory, and professional involvement in social justice are offered as methods to decrease bias in descriptive practices. A multi-method study was conducted consistent of a survey and a content analysis to analyze similarities and differences in how humanities scholars (users) and librarians and archivists (image catalogers) designate race through textual description and subject tagging. The results found that the 151 participants perceived race for African Americans through textual description 50% more than for White Americans. 80% of participants designated race for African Americans through subject tagging, while 12% designated race for White Americans, suggesting participants' acceptance of a default status of White Americans

A new model for semantic photograph description combining basic levels and user-assigned descriptors

Few studies have been conducted to identify users’ desired semantic levels of image access when describing, searching, and retrieving photographs online. The basic level, or the level of abstraction most commonly used to describe an item, is a cognitive theory currently under consideration in image retrieval research. This study investigates potential basic levels of description for online photographs by testing the Hierarchy for Online Photograph Representation (HOPR) model, which is based on a need for a model that addresses users’ basic levels of photograph description and retrieval. We developed the HOPR model using the following three elements as guides: the most popular tags of all time on Flickr, the Pyramid model for visual content description by Jörgensen, Jaimes, Benitez, and Chang, and the nine classes of image content put forth by Burford, Briggs, and Eakins. In an exploratory test of the HOPR model, participants were asked to describe their first reaction to, and possible free-text indexing terms for, a small set of personal photographs. Content analysis of the data indicated a clear set of user preferences that are consistent with prior image description studies. Generally speaking, objects in the photograph and events taking place in the photograph were the most commonly used levels of description. The preliminary HOPR model shows promise for its intended utility, but further refinement is needed through additional research

Finding emotional-laden resources on the World Wide Web

Some content in multimedia resources can depict or evoke certain emotions in users. The aim of Emotional Information Retrieval (EmIR) and of our research is to identify knowledge about emotional-laden documents and to use these findings in a new kind of World Wide Web information service that allows users to search and browse by emotion. Our prototype, called Media EMOtion SEarch (MEMOSE), is largely based on the results of research regarding emotive music pieces, images and videos. In order to index both evoked and depicted emotions in these three media types and to make them searchable, we work with a controlled vocabulary, slide controls to adjust the emotions’ intensities, and broad folksonomies to identify and separate the correct resource-specific emotions. This separation of so-called power tags is based on a tag distribution which follows either an inverse power law (only one emotion was recognized) or an inverse-logistical shape (two or three emotions were recognized). Both distributions are well known in information science. MEMOSE consists of a tool for tagging basic emotions with the help of slide controls, a processing device to separate power tags, a retrieval component consisting of a search interface (for any topic in combination with one or more emotions) and a results screen. The latter shows two separately ranked lists of items for each media type (depicted and felt emotions), displaying thumbnails of resources, ranked by the mean values of intensity. In the evaluation of the MEMOSE prototype, study participants described our EmIR system as an enjoyable Web 2.0 service

Journalistic image access : description, categorization and searching

The quantity of digital imagery continues to grow, creating a pressing need to develop efficient methods for organizing and retrieving images. Knowledge on user behavior in image description and search is required for creating effective and satisfying searching experiences. The nature of visual information and journalistic images creates challenges in representing and matching images with user needs. The goal of this dissertation was to understand the processes in journalistic image access (description, categorization, and searching), and the effects of contextual factors on preferred access points. These were studied using multiple data collection and analysis methods across several studies. Image attributes used to describe journalistic imagery were analyzed based on description tasks and compared to a typology developed through a meta-analysis of literature on image attributes. Journalistic image search processes and query types were analyzed through a field study and multimodal image retrieval experiment. Image categorization was studied via sorting experiments leading to a categorization model. Advances to research methods concerning search tasks and categorization procedures were implemented. Contextual effects on image access were found related to organizational contexts, work, and search tasks, as well as publication context. Image retrieval in a journalistic work context was contextual at the level of image needs and search process. While text queries, together with browsing, remained the key access mode to journalistic imagery, participants also used visual access modes in the experiment, constructing multimodal queries. Assigned search task type and searcher expertise had an effect on query modes utilized. Journalistic images were mostly described and queried for on the semantic level but also syntactic attributes were used. Constraining the description led to more abstract descriptions. Image similarity was evaluated mainly based on generic semantics. However, functionally oriented categories were also constructed, especially by domain experts. Availability of page context promoted thematic rather than object-based categorization. The findings increase our understanding of user behavior in image description, categorization, and searching, as well as have implications for future solutions in journalistic image access. The contexts of image production, use, and search merit more interest in research as these could be leveraged for supporting annotation and retrieval. Multiple access points should be created for journalistic images based on image content and function. Support for multimodal query formulation should also be offered. The contributions of this dissertation may be used to create evaluation criteria for journalistic image access systems