TAG ME: An Accurate Name Tagging System for Web Facial Images using Search-Based Face Annotation

Now a day the demand of social media is increases rapidly and most of the part of social media is made up of multimedia content cognate as images, audio, video. Hence for taking this as a motivation we have proffer a framework for Name tagging or labeling For Web Facial Images, which are easily obtainable on the internet. TAG ME system does that name tagging by utilizing search-based face annotation (SBFA). Here we are going to select an image from a database which are weakly labeled on the internet and the "TAG ME" assign a correct and accurate names or tags to that facial image, for doing this a few challenges have to be faced the One exigent difficulty for search-based face annotation strategy is how to effectually conduct annotation by utilizing the list of nearly all identical face images and its labels which is weak that are habitually rowdy and deficient. In TAGME we have resolve this problem by utilizing an effectual semi supervised label refinement (SSLR) method for purify the labels of web and nonweb facial images with the help of machine learning techniques. Secondly we used convex optimization techniques to resolve learning problem and used effectual optimization algorithms to resolve the learning task which is based on the large scale integration productively. For additionally quicken the given system, finally TAGME system proposed clustering-based approximation algorithm which boost the scalability considerably

A Large-Scale Database of Images and Captions for Automatic Face Naming

We present a large scale database of images and captions, designed for supporting research on how to use captioned images from the Web for training visual classifiers. It consists of more than 125,000 images of celebrities from different fields downloaded from the Web. Each image is associated to its original text caption, extracted from the html page the image comes from. We coin it FAN-Large, for Face And Names Large scale database. Its size and deliberate high level of noise makes it to our knowledge the largest and most realistic database supporting this type of research. The dataset and its annotations are publicly available and can be obtained from http://www.vision.ee.ethz.ch/~calvin/fanlarge/. We report results on a thorough assessment of FAN-Large using several existing approaches for name-face association, and present and evaluate new contextual features derived from the caption. Our findings provide important cues on the strengths and limitations of existing approaches

Navigating the Kaleidoscope of Object(ive)s: A User-Experience Approach to Cultural-Historical Activity Theory

Activity Theory, specifically third-generation activity theory also known as Cultural-Historical Activity Theory or CHAT (Engeström, 2001, 2015; Leontiev, 1978, 1981; Vygotsky, 1978) has largely been used as a framework for studying different networks of activity, encountered by subjects who utilize tools or mediating artifacts in order to divide their labor within particular communities. This theoretical and empirical project analyzes a transnational user’s experiences performing their identity on Instagram by answering the research question: How does a user with transnational literacy experiences perform their identity and manage communities through the mediation of particular technologies on Instagram? Using mixed-methods from four data streams—1) semi-structured interviews, 2) rhetorical analysis of a participant’s personal Instagram data (including images, captions, account biographies, and stories), 3) recordings of a participant using think-aloud protocol, and 4) analytical memos of the participant’s Instagram activity—in this thesis project I aimed to accomplish three goals. First, to outline and historicize influential generations of Activity Theory. Second, to present a new approach to Cultural-Historical Activity Theory called the “User-Experience CHAT Model.” Third, to apply the new model to a case study. The results of the study suggest that users on social media sites may communicate with particular communities, but also past, present, and future versions of themselves. As users engage in activities across time, they encounter a field of interpretation informed by contexts, which influence their present experiences as they produce an object. Thus, users’ identities are constantly in a state of transformation and becoming as their object(ive)s in social media activities transform across time

A Large-Scale Database of Images and Captions for Automatic Face Naming

We present a large scale database of images and captions, designed for supporting research on how to use captioned images from the Web for training visual classifiers. It consists of more than 125,000 images of celebrities from different fields downloaded from the Web. Each image is associated to its original text caption, extracted from the html page the image comes from. We coin it FAN-Large, for Face And Names Large scale database. Its size and deliberate high level of noise makes it to our knowledge the largest and most realistic database supporting this type of research. The dataset and its annotations are publicly available and can be obtained from http://www.vision. ee.ethz.ch/∼calvin/fanlarge/. We report results on a thorough assessment of FAN-Large using several existing approaches for name-face association, and present and evaluate new contextual features derived from the caption. Our findings provide important cues on the strengths and limitations of existing approaches. © 2011. The copyright of this document resides with its authors

Moron, Haiti after the hurricane Matthew : Creating place in news images

Pro gradu -tutkielmani käsittelee visuaalista ja kielellistä viestintää ja sitä, mitä todellisuuksia ja merkityksiä ne luovat yhdessä. Tutkimukseni aihe on ranskalaisen Le Monde-lehden verkkoversio ja siinä julkaistu uutistarina Haitin yli pyyhkineestä Matthew-hurrikaanista lokakuussa 2016. Tutkimusmateriaalina toimivat verkkolehdessä julkaistut uutiskuvat ja niiden kuvatekstit. Multimodaalisen analyysin avulla tutkin, mitä kuvat ja sanat viestivät yhdessä, niitä tuottaneiden toimittajien, kuvaajien ja toimituksen avulla. Tutkielman ytimessä on yhteiskuntatieteiden, erityisesti ihmismaantieteen paikka-käsite. Paikka, on tässä kontekstissa enemmän kuin arkikielen piste kartalla. Se on käsite, jolla tutkitaan merkityksiä, muistoja, ja jäsennellään maailmaa. Tutkimuskysymys onkin: millaista paikkaa uutiskuvissa ja niiden kuvateksteissä rakennetaan? Ranskalaisen valtavirtamedian kertomassa tarinassa maan entisestä siirtomaasta Haitista, sanan ja vallan aspekti on myös otettu huomioon. Pääasiallisen materiaalin, eli uutiskuvien, takana olevat kuvaajat ovat kuitenkin paikallisia. Tämä on otettu huomioon tutkielman analyysissä. Tutkimusmateriaalista löytyi erilaisia kategorioita, joiden mukaan kuvat on lajiteltu ja analysoitu. Hurrikaanin vavisuttama Haiti näyttäytyy niin tuhon, pelastuksen kuin toivonkin paikkana, jota katselevat ja tulkitsevat niin paikalliset kuin ulkopuolisetkin silmät

Creating a test collection to evaluate diversity in image retrieval

This paper describes the adaptation of an existing test collection for image retrieval to enable diversity in the results set to be measured. Previous research has shown that a more diverse set of results often satisfies the needs of more users better than standard document rankings. To enable diversity to be quantified, it is necessary to classify images relevant to a given theme to one or more sub-topics or clusters. We describe the challenges in building (as far as we are aware) the first test collection for evaluating diversity in image retrieval. This includes selecting appropriate topics, creating sub-topics, and quantifying the overall effectiveness of a retrieval system. A total of 39 topics were augmented for cluster-based relevance and we also provide an initial analysis of assessor agreement for grouping relevant images into sub-topics or clusters

Visual Representations of Gender and Computing in Consumer and Professional Magazines

Studies in the nineteen-eighties showed that advertising images of computers were gendered, with women relatively less represented, and shown with less empowered roles, problems or presented as sexual objects. This paper uses a mix of content and interpretative analysis to analyse current imagery in consumerist and professional society publications. It reveals the present variation and complexity of the iconography of computers and people across different domains of representation, with the continuation of gender bias in subtle forms

Γλύκοπικρος & Bittersweet: An Autoethnographic Approach to Studying Abroad in Greece

The purpose of this study is to answer the question: How can reflection via an autoethnographic approach promote sought-after outcomes of a semester studying abroad? Through an anthropological lens, I completed field work, kept field notes, and wrote a reflexive blog to navigate the social processes of learning to belong in another place within the context of a multicultural environment of study abroad program with Erasmus students. Through autoethnography as a methodology and a text, I utilized linguistic analysis to identify key themes that represent my transformative experience. The personal, emotional, and intellectual growth I experienced was made transformative by double vision and long-term engagement with the autoethnographic text. In Part I, I introduce the research project at the center of this thesis. Following the first section, I include a review of scholarship on autoethnography, affect and reflexivity–three areas of focus that emerged as I aimed to understand my own learning through the study abroad experience. In the third section of my thesis, I summarize the methodology and explain my decisions in conducting the approaches to research and analysis. Following the discussion of my methodology are the findings of my autoethnography (Part IV) in which I identify themes of kinship, place & home, identity, language, and Erasmus culture. I conclude (in Part V) with a discussion of the results, placing my research into dialogue with some of the research on fostering learning through study abroad

Language and Perceptual Categorization in Computational Visual Recognition

Computational visual recognition or giving computers the ability to understand images as well as humans do is a core problem in Computer Vision. Traditional recognition systems often describe visual content by producing a set of isolated labels, object locations, or by even trying to annotate every pixel in an image with a category. People instead describe the visual world using language. The rich visually descriptive language produced by people incorporates information from human intuition, world knowledge, visual saliency, and common sense that go beyond detecting individual visual concepts like objects, attributes, or scenes. Moreover, due to the rising popularity of social media, there exist billions of images with associated text on the web, yet systems that can leverage this type of annotations or try to connect language and vision are scarce. In this dissertation, we propose new approaches that explore the connections between language and vision at several levels of detail by combining techniques from Computer Vision and Natural Language Understanding. We first present a data-driven technique for understanding and generating image descriptions using natural language, including automatically collecting a big-scale dataset of images with visually descriptive captions. Then we introduce a system for retrieving short visually descriptive phrases for describing some part or aspect of an image, and a simple technique to generate full image descriptions by stitching short phrases. Next we introduce an approach for collecting and generating referring expressions for objects in natural scenes at a much larger scale than previous studies. Finally, we describe methods for learning how to name objects by using intuitions from perceptual categorization related to basic-level and entry-level categories. The main contribution of this thesis is in advancing our knowledge on how to leverage language and intuitions from human perception to create visual recognition systems that can better learn from and communicate with people.Doctor of Philosoph