  This philosophical paper proposes a modified version of the scientific
method, in which large databases are used instead of experimental observations
as the necessary empirical ingredient. This change in the source of the
empirical data allows the scientific method to be applied to several aspects of
physical reality that previously resisted systematic interrogation. Under the
new method, scientific theories are compared by instantiating them as
compression programs, and examining the codelengths they achieve on a database
of measurements related to a phenomenon of interest. Because of the
impossibility of compressing random data, "real world" data can only be
compressed by discovering and exploiting the empirical structure it exhibits.
The method also provides a new way of thinking about two longstanding issues in
the philosophy of science: the problem of induction and the problem of
demarcation.
  The second part of the paper proposes to reformulate computer vision as an
empirical science of visual reality, by applying the new method to large
databases of natural images. The immediate goal of the proposed reformulation
is to repair the chronic difficulties in evaluation experienced by the field of
computer vision. The reformulation should bring a wide range of benefits,
including a substantially increased degree of methodological rigor, the ability
to justify complex theories without overfitting, a scalable evaluation
paradigm, and the potential to make systematic progress. A crucial argument is
that the change is not especially drastic, because most computer vision tasks
can be reformulated as specialized image compression techniques. Finally, a
concrete proposal is discussed in which a database is produced by recording
from a roadside video camera, and compression is achieved by developing a
computational understanding of the appearance of moving cars.
