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Google Tech Talk ( info ) May 5, 2011 Presented Professor Fei-Fei Li, Stanford University ABSTRACT A key challenge visual recognition recognize label large number visual concepts, object scene classes. In talk, I'll discuss projects Stanford Vision Lab topic. ImageNet large-scale image ontology built backbone structure WordNet. In talk, show briefly ImageNet constructed Amazon Mechanical Turk. And largest (publicly ) dataset tens thousands image classes, today' state---art computer vision algorithms problem large-scale image classification? Then I discuss image representation called "Object Bank", significant departure previous image representation techniques Bag--Words models -level features (SIFT, HOG, GIST, .). We show Object Bank representation, simple linear SVM classifier result superior performances standard image classification datasets. Furthermore, sparsity algorithms representation efficient scalable large scene datasets, reveal semantically meaningful feature patterns.