Semantic Tagging on Historical Maps

Tags assigned by users to shared content can be ambiguous. As a possible solution, we propose semantic tagging as a collaborative process in which a user selects and associates Web resources drawn from a knowledge context. We applied this general technique in the specific context of online historical maps and allowed users to annotate and tag them. To study the effects of semantic tagging on tag production, the types and categories of obtained tags, and user task load, we conducted an in-lab within-subject experiment with 24 participants who annotated and tagged two distinct maps. We found that the semantic tagging implementation does not affect these parameters, while providing tagging relationships to well-defined concept definitions. Compared to label-based tagging, our technique also gathers positive and negative tagging relationships. We believe that our findings carry implications for designers who want to adopt semantic tagging in other contexts and systems on the Web.Comment: 10 page

Towards Understanding User Preferences from User Tagging Behavior for Personalization

Personalizing image tags is a relatively new and growing area of research, and in order to advance this research community, we must review and challenge the de-facto standard of defining tag importance. We believe that for greater progress to be made, we must go beyond tags that merely describe objects that are visually represented in the image, towards more user-centric and subjective notions such as emotion, sentiment, and preferences. We focus on the notion of user preferences and show that the order that users list tags on images is correlated to the order of preference over the tags that they provided for the image. While this observation is not completely surprising, to our knowledge, we are the first to explore this aspect of user tagging behavior systematically and report empirical results to support this observation. We argue that this observation can be exploited to help advance the image tagging (and related) communities. Our contributions include: 1.) conducting a user study demonstrating this observation, 2.) collecting a dataset with user tag preferences explicitly collected.Comment: 6 page

Automatic Synchronization of Multi-User Photo Galleries

In this paper we address the issue of photo galleries synchronization, where pictures related to the same event are collected by different users. Existing solutions to address the problem are usually based on unrealistic assumptions, like time consistency across photo galleries, and often heavily rely on heuristics, limiting therefore the applicability to real-world scenarios. We propose a solution that achieves better generalization performance for the synchronization task compared to the available literature. The method is characterized by three stages: at first, deep convolutional neural network features are used to assess the visual similarity among the photos; then, pairs of similar photos are detected across different galleries and used to construct a graph; eventually, a probabilistic graphical model is used to estimate the temporal offset of each pair of galleries, by traversing the minimum spanning tree extracted from this graph. The experimental evaluation is conducted on four publicly available datasets covering different types of events, demonstrating the strength of our proposed method. A thorough discussion of the obtained results is provided for a critical assessment of the quality in synchronization.Comment: ACCEPTED to IEEE Transactions on Multimedi

Social Tagging: Exploring the Image, the Tags, and the Game

An increasing amount of images are being uploaded, shared, and retrieved on the Web. These large image collections need to be properly stored, organized and easily retrieved. Tags have a key role in image retrieval but it is difficult for those who upload the images to also undertake the quality tag assignment for potential future retrieval by others. Relying on professional keyword assignment is not a practical option for large image collections due to resource constraints. Although a number of content-based image retrieval systems have been launched, they have not demonstrated sufficient utility on large-scale image sources on the web, and are usually used as a supplement to existing text-based image retrieval systems. An alternative to professional image indexing can be social tagging -- with two major types being photo-sharing networks and image labeling games. Here we analyze these applications to evaluate their usefulness from the semantic point of view. We also investigate whether social tagging behaviour can be managed. The findings of the study have shown that social tagging can generate a sizeable number of tags that can be classified as interpretive for an image, and that tagging behaviour has a manageable and adjustable nature depending on tagging guidelines

Facebook Photo Tagging Culture and Practices Among Digital Natives

Social Networking Services (SNS) have gained immense popularity in developing countries like India,where digital natives are actively communicating on these platforms. Understanding the interactionbetween technology systems and digital natives, and proposing guidelines and recommendations for thedevelopment of better systems is highly valuable. Prior research examining users’ motivations and actualusage of photo tagging systems is limited, and predominately focused on Flickr and adult users. In order tounderstand in detail why, how, and with whom users tag digital photos on Facebook, a qualitative essaybasedexploratory study is organized with 67 digital natives in India. The study aims to build understandingof the various gratifications, motivations, experiences, and practices associated with Facebook phototagging, focusing on technologically savvy Indian digital natives. Our results reveal that photo taggingpractices by digital natives vary substantially, especially among gender groups. Facebook photo tagging ispopular among Indian boys, and they are more willing to embrace and use it. Meanwhile, involvement ofIndian girls is considerably limited, as they tend to avoid Facebook photo tagging, mainly due to privacyconcerns, as well as social norms and pressures.Peer reviewe

Labeling Faces Victimization Bunch Primarily Based Internet Pictures Annotation to Produce Authentication in Security

Auto face annotation is important in abounding absolute apple advice administration systems. Face tagging in images and videos enjoys abounding abeyant applications in multimedia advice retrieval. Face comment is a meadow of face apprehension and recognition. Mining abominably labeled facial images on the internet shows abeyant classic appear auto face annotation. This blazon of classic motivates the new assay botheration of defended authentication. The ambition of the arrangement is to comment disregarded faces in images and videos with the words that best alarm the image. A framework called seek based face comment (SBFA) provides the way to abundance abominably labeled facial images. Facial images that are accessible on Apple Wide Web (WWW) or the angel database created by the aegis administration can be annotated. A one arduous botheration with the seek based face comment arrangement is how finer accomplish comment by advertisement agnate facial images and their anemic labels which are blatant and incomplete. To affected this botheration proposed admission uses unsupervised characterization clarification (ULR) to clarify the labels of web facial images. To acceleration up the proposed arrangement a absorption based approximation algorithm is used. Uses of comment will advice for user to seek admiration angel and video. As well if arrangement gets implemented in amusing arrangement again it will affected the check of accepted absolute arrangement which tags manually

Mining social media to create personalized recommendations for tourist visits

International audiencePhoto sharing platforms users often annotate their trip photos with landmark names. These annotations can be aggregated in order to recommend lists of popular visitor attractions similar to those found in classical tourist guides. However, individual tourist preferences can vary significantly so good recommendations should be tailored to individual tastes. Here we pose this visit personalization as a collaborative filtering problem. We mine the record of visited landmarks exposed in online user data to build a user-user similarity matrix. When a user wants to visit a new destination, a list of potentially interesting visitor attractions is produced based on the experience of like-minded users who already visited that destination. We compare our recommender to a baseline which simulates classical tourist guides on a large sample of Flickr users

Information extraction from multimedia web documents: an open-source platform and testbed

The LivingKnowledge project aimed to enhance the current state of the art in search, retrieval and knowledge management on the web by advancing the use of sentiment and opinion analysis within multimedia applications. To achieve this aim, a diverse set of novel and complementary analysis techniques have been integrated into a single, but extensible software platform on which such applications can be built. The platform combines state-of-the-art techniques for extracting facts, opinions and sentiment from multimedia documents, and unlike earlier platforms, it exploits both visual and textual techniques to support multimedia information retrieval. Foreseeing the usefulness of this software in the wider community, the platform has been made generally available as an open-source project. This paper describes the platform design, gives an overview of the analysis algorithms integrated into the system and describes two applications that utilise the system for multimedia information retrieval

Improving Digital Record Annotation Capabilities with Open-sourced Ontologies and Crowd-sourced Workers

The Museum of the City of New York has undertaken a long-term project to digitize its collection of 1.5 million objects, annotate them with metadata, and make them publicly available via the Internet. At present, Museum staff annotate images using a traditional lexicon assembled from authority sources such as the Library of Congress and the Getty Art and Architecture Thesaurus, but with limited resources the Museum cannot scale to meet its goal of providing the highest levels of accessibility and discoverability of collections to researchers as well as to the general public. This project offers a cost-effective, scalable solution that 1) consolidates the current lexicon with linked open data sources by generating alignments and reconciling semantically equivalent elements, creating a super-set lexicon, and 2) divides the work of annotating into micro-tasks that can be completed by huge labor pools available through crowd-sourced marketplaces