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.