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.
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.
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:
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!
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)
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)