To gain a better perspective on my research regarding the Visual Memex, I spent some time reading Object Categorization: Computer and Human Vision Perspectives which contains many lovely essays on Computer Vision. This book contains recently written essays by titans of Computer Vision and contains a great deal lessons learned from history. While such a 'looking back' on vision makes for a good read, it is also worthwhile to find old works 'looking forward' and anticipating the successes and failures of the upcoming generations.
In this 'looking forward' fashion, I want to share a passage regarding image understanding systems, from "Representation and Use of Knowledge in Vision," by H. G. Barrow and J. M. Tenenbaum, July 1975. This is a short paper worth reading for both graduate students and professors interested in pushing Computer Vision research to its limits. I enjoyed the succinct and motivational ending so much, it is worth repeating it verbatim:
We conclude by reiterating some of the major premises underlying this paper:
The more knowledge the better.
The more data, the better.
Vision is a gigantic optimization problem.
Segmentation is low-level interpretation using general knowledge.
Knowledge is incrementally acquired.
Research should pursue Truth, not Efficiency.
A further decade will determine our skill as visionaries.