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

Compression Rate Method for Empirical Science and Application to Computer Vision