Google image-swirl and some ideas

I always asked to myself why Google and other search engines do not offered to the user new interaction/visualization mechanisms whether there have been many initiatives in this field. Actually, one of the first papers appeared almost ten years ago [Chen00]. Frofesor Fabio said to me that new technologies take much time to appear due to they are subject to "the convergence". Well, he had reason ;). To my surprise, Google recently published a new demo lab tool, called image-swirl, that allow the user to visually interact with its large image repository. In this screen shot, I show an example of the system. This initiative is very nice and motivating for my research.

I was searching by "carcinoma". The interaction begins with a set of piles of images organized according to cluster of similar images. One you select one pile, it goes to the front (expanded in a circle) whilst the rest go to the background. While you interact with the collection, a spiral shape preserves the navigation path. This tool has what an Information Visualization (IV) metaphor must have, which Ben Shneiderman calls IV mantra: “Overview, zoom and filter, details on demand” [1].

A visualization metaphor depends on the domain context and on the task. I am not sure that it is at all suitable to medical domain. That is, imagine a pathology's student navigating a collection of basal cell carcinoma images in order to learn about the presence of a pathology concept. He/her probably will need a visualization/interaction tool that allow him/her to do this task easily and quickly. I think that is a good research question what is a good visualization metaphor to this case, and how it can improve learning tasks. We could address this question in a master thesis :).

It is difficult to build a good similarity function to this concept using only low-level features (color, textures, borders) or combination of them with a priori information provided by the expert. One possibility to reduce this semantic gap is trying to fusion different information sources (text, images, audio, etc.) to construct this similarity function (what Juanca is doing), and another possibility is to reduce this semantic gap but learning from the user interaction. That is, we begin with a basic similarity function and through the user interaction we adapt this similarity function.

I have been reviewing the literature from information retrieval perspective about learning the similarity function through user interaction. I found that this area has been very active since some years ago. Actually, I found some approaches where the kernel matrix changes in each iteration, and I also found some papers addressing this issue but in the medical context.

I will talk about my reviewing in more detail in the next post.

Enjoy image-swirl.

;)

[1] Ben Shneiderman. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proc. 1996 IEEE Symposium on Visual Languages (VL’96), pages 336–343. IEEE Computer Society, Boulder, Colorado. doi:10.1109/VL.1996.545307. http://citeseer.ist.psu.edu/shneiderman96eyes.html.