While Lev Manovich will reject a number of definitions in his reformation of “media visualization,” the project reaches a newly organic stage in the final examples of his “Visualizing Vertov” piece. The relationship between eye and interface, and the shifts in this relationship, undergird each of “Visualizing Vertov,” “Media Visualization” and “How to Compare One Million Images?” The focus, however, will remain visualizing data in a way to make it, once again, human scale. Something most human, I argue, occurs in the final examples of “Visualizing Vertov.”
In “One Million Images” Manovich focuses on the eye and interface but with a more sever critique of the eye. Not only is the eye not scalable to different data collections, but “[t]he second problem with using our eyes is that we are not very good at registering subtle differences between images.” Wisely, I think, he shifts to a complaint of the interface more singularly in “Media Visualization:” “Although it may appear that the reasons for this are the limitations of human vision and human information processing, I think that it is actually the fault of current interface designs…This access method does not allow us to understand the ‘shape’ of overall collection and notice interesting patters” [sic]. Here, we can again notice interesting patterns! Phew. The trick is to change the scale of the project to one at which we can use our apperceive apparatus to notice “shape.”
However, the idea of media visualization does not, I would argue, come to fruition until the last couple of examples in his “Visualizing Vertov” piece: “Anatomy of a Shot” and “Visualizing Movement.” Notably, figures 8.2 and 9.4. These images represent shots “averaged” into a single image.
The process works differently here than in the graphs and “montages” of images. Shots averaged together most completely fulfill the definition of “media visualization” in which “pictures are translated into pictures” but also, pictures, as well as they can, do not hide the original images of the media under consideration. Manovich makes a special note of this move away from this traditional graphs: “Typical information visualization involves first translating the world into numbers and then visualizing relations between these numbers. In contrast, media visualization involves translating a set of images into a new images which can reveal patterns in the set. In short, pictures are translated into pictures.” The result, at first and for a good while, is “images in a collection superimposed on a graph” (5). Such graphs offer little more information that the graph itself would, except perhaps to understand the axes more intuitively.
What seems, however, the final result of this project, most nuanced interpretation, and most engaging image built from images, are these composite images of shots. They do not, admittedly, have the potential to convey raw-ish information like the superimposed-upon graphs, but differently do more with the visual data. Namely, they seem to have the potential, of any of these visualizations, to tell us something we did not know, and to beg further questions, rather than, as Manovich often freely admits, verify claims. The image apprehends something itself of the shot beyond the algorithm used to generate it.
These approaches have problems, too. For example, with projects 8 and 9 in “Visualizing Vertov,” Manovich tacitly acknowledges the inability of such projects to scale (the problem in the other two pieces). In fact, the projects seem only to reveal movement at a single scale. If an object within a shot moves too quickly, the movement will be erased from the composite image. However, if an object moves too slowly, the object would appear stationary in the composite shot. In this way, an object’s ability to be perceived has a direct relationship with the “scale” of the data set, the scale here being the length of the shot. I note these particular examples, however, because these exact complications generated by the composite images offer the greatest potential for a future project.