Mining Events from Multimedia streams, Jon Hare

peaker(s): Jon Hare Organiser: Time: 25/06/2014 11:00-11:50 Location: B32/3077 Abstract The aggregation of items from social media streams, such as Flickr photos and Twitter tweets, into meaningful groups can help users contextualise and effectively consume the torrents of information on the social web. This task is challenging due to the scale of the streams and the inherently multimodal nature of the information being contextualised. In this talk I'll describe some of our recent work on trend and event detection in multimedia data streams. We focus on scalable streaming algorithms that can be applied to multimedia data streams from the web and the social web. The talk will cover two particular aspects of our work: mining Twitter for trending images by detecting near duplicates; and detecting social events in multimedia data with streaming clustering algorithms. I'll will describe in detail our techniques, and explore open questions and areas of potential future work, in both these tasks.

OpenImaj WebTools

A compiled JAR of the OpenImaj WebTools module. You can download this and run it straight away. See http://www.openimaj.org/ and http://blogs.ecs.soton.ac.uk/oneshare/2012/04/03/audio-proceedings/ for further details

WAIS seminar: Research Discussion: Lab of Things? What Things?

Wednesday 19th March 2014 Speaker(s): Kirk Martinez, Dr Jonathon S Hare and Dr Enrico Costanza Organiser: Dr Tim Chown Time: 19/03/2014 11:00-11:50 Location: B32/3077 File size: 676 Mb Abstract The new WAIS seminar series features classic seminars, research discussions, tutorial-style presentations, and research debates. This seminar takes the form of a research discussion which will focus on the Internet of Things (IoT) research being undertaken in WAIS and other research groups in ECS. IoT is a significant emerging research area, with funding for research available from many channels including new H2020 programmes and the TSB. We have seen examples of IoT devices being built in WAIS and other ECS groups, e.g. in sensor networking, energy monitoring via Zigbee devices, and of course Erica the Rhino (a Big Thing!). The goal of the session is to briefly present such examples of existing Things in our lab with the intent of seeding discussion on open research questions, and therefore future work we could do towards new Things being deployed for experimentation in Building 32 or its environs. The session will discuss what 'things' we have, how they work, what new 'things' might we want to create and deploy, what components we might need to enable this, and how we might interact with these objects.

What can Flickr photographs tell us about the world?

In their second year, our undergraduate web scientists undertake a group project module (WEBS2002, led by Jonathon Hare & co-taught by Su White) in which they get to apply what they learnt in the first year to a practical web-science problem, and also learn about team-working. For the project this semester, the students were provided with a large dataset of geolocated images and associated metadata collected from the Flickr website. Using this data, they were tasked with exploring what this data could tell us about the world. In this seminar the two groups will present the outcomes of their work. Team Alpha (Ellie Hamilton, Clayton Jones & Alok Acharya) will present their work on "The relationship between Group Photos, Social Integration and Suicide". This work aims to explore whether levels of social integration (which Durkheim posited as a factor in "Egoistic Suicide" rates) can be predicted by measuring the proportion of photos of groups of people to photos of individuals within a geographical region. Team Bravo (Agnieszka Grzesiuk-Szolucha, Thomas Leese & Ammaar Tawil) will present their work on "Sentiment Analysis on Flickr Photo Tags to Classify a Photo as Positive or Negative, In Order to Determine the Happiness of a Country or Region". This work explores whether estimates of sentiment made by applying SentiWordNet to Flickr tags correlate with indices of world happiness and socio-economic well-being.

CSS Practical

The CSS that we wrote in the lecture, applied to the updated Tortoise and Hare HTML story created in week 1. (Note that there's 3 different stylesheets attached. Use View > Page Styles to see them all in Firefox.) Also links to Zen Garden so you can do the group task.

What can Flickr photographs tell us about New York City?

In their second year, our undergraduate web scientists undertake a group project module (WEBS2002, led by Jonathon Hare & co-taught by Su White) in which they get to apply what they learnt in the first year to a practical web-science problem, and also learn about team-working. For the project this semester, the students were provided with a large dataset of geolocated images and associated metadata collected from the Flickr website. Using this data, they were tasked with exploring what this data could tell us about New York City. In this seminar the two groups will present the outcomes of their work. Team Alpha (Wil Muskett, Mark Cole & Jiwanjot Guron) will present their work on "An exploration of deprivation in NYC through Flickr". This work aims to explore whether social deprivation can be predicted geo-spatially through the analysis of social media by exploring correlations within the Flickr data against official statistics including poverty indices and crime rates. Team Bravo (Edward Baker, Callum Rooke & Rachel Whalley) will present their work on "Determining the Impact of the Flickr Relaunch on Usage and User Behaviour in New York City". This work explores the effect of the Flickr site relaunch in 2013 and looks at how user demographics and the types of content created by the users changed with the relaunch.

ImageLearn - Decoding Britain's Landscape

Abstract Ordnance Survey, our national mapping organisation, collects vast amounts of high-resolution aerial imagery covering the entirety of the country. Currently, photogrammetrists and surveyors use this to manually capture real-world objects and characteristics for a relatively small number of features. Arguably, the vast archive of imagery that we have obtained portraying the whole of Great Britain is highly underutilised and could be ‘mined’ for much more information. Over the last year the ImageLearn project has investigated the potential of "representation learning" to automatically extract relevant features from aerial imagery. Representation learning is a form of data-mining in which the feature-extractors are learned using machine-learning techniques, rather than being manually defined. At the beginning of the project we conjectured that representations learned could help with processes such as object detection and identification, change detection and social landscape regionalisation of Britain. This seminar will give an overview of the project and highlight some of our research results.