New version of MedViz

I published a new version of the MedViz system.

MedViz is a web tool for visualizing and exploring large collection of medical images. In this prototype, I provide interaction functionalities that allow the user to navigate the collection.

Functionality
* In the screen are displayed 100 images of the Corel data set (2500 images)
* Select one image as relevant (dblclick)
* A new summary is generated with the "Start again" button
* Retrieve images pressing the "Search" button
* The system will retrieve new images according to the images selected as relevants by the user

Interacción2009

This week I am attending to the Interacción2009 conference. My presentation was nice. In general, the conference topics are related to HCI theory, usability, collaborative work and awareness. My talk was a little different because it had machine learning and maths equations, I think that it was interesting to the people. Cesar Collazos, the conference chair, suggested to me the following ideas:

• Collaborative image collection annotation to improve the image collectio visualization
• New interfaces to visualize the image collection, for example, mobile devices such as iPhone, etc.

Well, tomorrow more conference news.

Informed meeting

Today we were working with informed development team. We defined some activities to start a new project phase. Main tasks for next days are:

• Upload information to google code project (http://code.google.com/p/informedproject/)
• Publish source code to SVN with the defined structure
• Linux re-installation in Condor
• System deployment to Condor
• To deal with server relocation (Telemedicin)

I created a new project in dotProject called Informed with these activities. Next meeting will be at September 17.

Relevance feedback

Well, this week I have been reading some papers about relevance feedback for image collection exploration and I find some interesting works in this address. The papers can be found in CiteULike (http://www.citeulike.org/user/camargoj). For me, the most interesting work was published by NGuyen at ACM Transactions on Multimedia last year (2008): "Optimization of Interactive Visual-Similarity-Based Search". Basically, authors propose a system that uses active learning for interacting with visualization of image collections. The build a summarization that is visualized to user, the user selects which images are relevant, the system applies a learning technique selection (SVMs), the system then retrieve new images according to the user feedback, the user repeat the process until target images are found. They did experiments with some image data sets and evaluate recall vs number of iterations.

I am working in Exploratory image collection search using a kernel-based relevance feedback model. I expect to have a more clear idea for sharing it with my advisor before the end of this week.