My guess would be that this kind of approach will only be partly successful, since fundamentally it's only based upon an elaborate kind of 2D template matching. I think what actually happens is that during early childhood experience we are able to statistically correlate certain types of geometry with the patterns of light falling upon out retinas. When we later view flat images we're able to retrieve the associated type of geometry and imagine what the object might look like from various angles, even if we have only seen it once. I expect that biological vision systems are fundamentally designed for 3D understanding of the world, since this is of high adaptive value, rather than a sort of 2D "screen scraping" or the retina.
2008/5/2 J Storrs Hall, PhD <[EMAIL PROTECTED]>: > Just saw this announcement go by: > > Abstract: > > Constructing ImageNet > > Data sets are essential in computer vision and content based image retrieval > research. We > present the work in progress for constructing ImageNet, a large scale image > data set based > on the Princeton WordNet. > The goal is to associate more than 1000 clean images with each node of > WordNet, which > consists of ~30,000 ( estimated ) imagable nodes. We build a prototype system > for > constructing ImageNet, as a first step toward large scale deployment. For > each > node of > WordNet, which is a synonym set (synset) for a single concept, we collect > candidate images > from the Internet and clean up them with semi-automatic labeling. We train > boosting > classifiers from human labeled data and use active learning to substantially > speed up the > labeling process. We also developed a web interface for massive online human > labeling. We > demonstrate the effectiveness of our system with results from a subset of > synsets. > > Reading list: > > Text book: > > Pattern Recognition and Machine Learning, Christopher M. Bishop, 2006. > Chapter 1,2,8,14. > Modern Operating System, Tanenbaum. > > Papers: > Animals on the Web, Berg, Forsyth, CVPR06 > OPTIMOL: automatic Online Picture collecTion via Incremental MOdel Learning, > Li, Wang, > Fei-Fei, CVPR07 Learning Object Categories from Google's image Search, > Fergus, > Fei-Fei, > Perona, Zissermaman, ICCV05 Harvesting Image Databases from the Web, Scroff, > Zisserman, > ICCV07 > From Aardvark to Zorro: A Benchmark of Mammal Images, Fink, Ullman, > NIPS05 > Tiny Images, Torralba, Fergus, Freeman, TechReport MIT, 2007 Labeling Images > with a > Computer Game. Luis von Ahn and Laura Dabbish, CHI04 > LabelMe: a database and web-based tool for image annotation, Russell, > Torralba, IJCV07 > Introduction to a large scale general purpose groundtruth dataset: > methodology, annotation tool, and benchmarks, Z.Y. Yao, X. Yang, and S.C. > Zhu, > EMMCVPR07 > Combining active and semi-supervised learning for spoken language > understanding, Tur, > Hakkani-Tur, Schapire, Speech Communication, 05 Online boosting and vision, > CVPR06 > > ------------------------------------------- > agi > Archives: http://www.listbox.com/member/archive/303/=now > RSS Feed: http://www.listbox.com/member/archive/rss/303/ > Modify Your Subscription: http://www.listbox.com/member/?&; > Powered by Listbox: http://www.listbox.com > ------------------------------------------- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244&id_secret=101455710-f059c4 Powered by Listbox: http://www.listbox.com
