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img(Anaktisi) Image Search Engine

Mathias Lux — Wed, 27 Feb 2008 09:00:19 +0000

Since Blobworld was down I was hoping for a new online CBIR system to show in lectures. Last week I received word about the img(Anaktisi) image search engine from Savvas Chatzichristofis. This search engine is based on 2 new content based descriptors that combine color and edge features. The results look promising!

There is also an offline sketch based retrieval system being developed. A screencast can be found on YouTube.

Related Links

img(Anaktisi) online image search
img(Paint.Anaktisis) offline sketch based cbir screencast (via YouTube)
Savvas’ blog

TIR 07: Call for Participation

Mathias Lux — Mon, 13 Aug 2007 21:00:25 +0000

On 3rd September the Workshop on Text Information Retrieval (TIR 07) takes place in Regensburg, Germany. I’ll be there presenting our work (Michael Granitzer, Roman Kern and me) on folksonomy characteristics: Aspects of Broad Folksonomies (download pdf). Main contribution of the paper is that tag similarity in the del.icio.us folksonomy follows a power law in many cases. This means that many tags have only few highly related tags, while the similarity to other tags is rather low.

You can find the whole program as well as the contributions (as pdf) on the TIR07 website.

WWW 2006 Tagging Workshop Proceedings [Update]

Mathias Lux — Mon, 23 Jul 2007 09:37:19 +0000

As Frank Smadja pointed out here (see comment) the original proceedings of the tagging workshop @ WWW 2006 site is down. Luckily the files are not lost, but mirrored here:

WWW 2006 Tagging Workshop Online Proceedings

Book Review: Google’s Pagerank and Beyond …

Mathias Lux — Thu, 24 May 2007 10:36:30 +0000

As I really like the field of information retrieval, I ordered a new book for our University library: Google’s Pagerank and Beyond: The Science of Search Engine Rankings (Amy N. Langville & Carl D. Meyer, University Presses of CA). Wondering if this was a good one I took a look inside …

I’ve got to say: I’m impressed! All the main ideas of information retrieval and PageRank (as well as HITS) are described within the first few chapters. The following chapters go into mathematical and technical details: Why and how does this work, why and how can it be used in a distributed environment? My opinion: Two thumbs up!

WWW 2006 Tagging Workshop Proceedings

Mathias Lux — Wed, 11 Apr 2007 10:02:42 +0000

At the WWW 2006 a workshop on the topic tagging took place. There were several papers especially on folksonomies and emergent semantics. ~~The proceedings are available online at: /hosted/taggingws-www2006-files/taggingworkshopschedule.htm~~ The proceedings are hosted here as the original site is no longer available. (Many thanks to Frank Smadja, who provided the files and allowed to mirror them)

Keynote

Tagging : From the personal to the social, Rashmi Sinha

Overview and Enterprise

Position Paper, Tagging, Taxonomy, Flickr, Article, ToRead.
Cameron Marlow, Mor Naaman, danah boyd, Marc Davis; Yahoo! Research and UC Berkeley School of Information Management & Systems.
Fringe Contacts: People-Tagging for the Enterprise.
Stephen Farrell and Tessa Lau IBM Almaden Research Center
Collaborative Tagging and Expertise in the Enterprise
Ajita John, Doree Seligmann, Avaya Labs Research.
Onomi: Social Bookmarking on a Corporate Intranet.
Laurie Damianos, John Griffith, Donna Cuomo; The MITRE Corporation

Mining the TagSpace

“Automated Tag Clustering: Improving search and exploration in the tag space.”
Grigory Begelman, Philipp Keller, Frank Smadja; Technion Israel Institute of Technology, Citrin Informatik GmbH and RawSugar.
“Towards the Semantic Web: Collaborative Tag Suggestions.”
Zhichen Xu, Yun Fu, Jianchang Mao, and Difu Su; Yahoo! Inc.
“Integrating Collaborative Tagging and Emergent Semantics for Image Retrieval.”
Melanie Aurnhammer, Luc Steels, Peter Hanappe; Sony Computer Science Laboratory Paris and University of Brussels (VUB).
“Inducing Ontology from Flickr Tags.”
Patrick Schmitz University of California, Berkeley and Yahoo! Research, Berkeley.

Applications

Foragr: Collaboratively Tagged Photographs and Social Information Visualization
Brian M. Dennis, EECS Department and New Media Program Northwestern University
Investigating social tagging and folksonomy in art museums with steve.museum
Jennifer Trant, Bruce Wyman; Archives & Museum Informatics, Faculty of Information Studies, University of Toronto, Denver Art Museum with the participants in the steve.museum project.
Structured vs. unstructured tagging ï¿½ A case study
Judit Bar-Ilan, Snunith Shoham, Asher Idan, Yitzchak Miller and Aviv Shachak; Department of Information Science, Bar-Ilan University Ramat-Gan, Israel.
Tagging the physical world
Yoelle S. Maarek, Natalia Marmasse, Yaakov Navon, Vova Soroka, Google and IBM Haifa Research Lab

Demos

Tressel: Semantic markup of RSS feeds
Brian McLernon Nicholas Kushmerick, School of Computer Science and Informatics, University College Dublin, Ireland
Implicit Tagging using Donated Bookmarks
Ben Markines, Lubomira Stoilova, and Filippo Menczer; Department of Computer Science, School of Informatics, Indiana University
Tag-Based Navigation for Peer-to-Peer Wikipedia; Jenneke Fokker, Johan Pouwelse and Wray Buntine; Departments of Industrial Design and Computer Sciences Delft University of Technology The Netherlands and Department of Computer Science, Helsinki Institute of Information Technology

How users think about image search …

Mathias Lux — Fri, 06 Apr 2007 11:00:41 +0000

There is an article about some new image classification and labeling system online: The UC San Diego has developed (together with Google, they brought in the data) a machine learning approach for automatic image labeling. Find the article here.

Well this is not the first image analysis engine that was created and it won’t be the last, but the discussion at slashdot was particulary funny. Some excerpts go here:

I remember when we had to go to a gas station and *buy* porn. Now you have computers out there finding porn for you. You kids today have it too easy!
If this doesn’t revolutionize the searching of online porn galleries, I don’t know what will.
… was similarly trained to recognize tanks in landscapes. [...] Then they introduced it to a new batch of images and it fell apart. Turns out that the initial set of images had all the tanks shot on a sunny day and all the tankless images shot on a cloudy day (or vice versa). It had learned to tell a sunny day from a cloudy day. Ha ha.
Now I can search for porn stars that look like that girl in my English class!

Thanks to social software we now know how the ordinary talkative geek thinks about image search

(thx to Roman for the hint on the article & the discussion)