Large-scale image classification: IMAGEnet and object database

!±8± Large-scale image classification: IMAGEnet and object database

Google Tech Talk (more information below) May 5, 2011 by Professor Fei-Fei Li Presented to recognize Stanford University ABSTRACT A central challenge in visual detection and identification of a variety of visual concepts, such as object recognition and scene classes. In this talk I will discuss two recent projects in the Stanford Vision Laboratory on this subject. IMAGEnet ontology is a great image that is built on the structure of WordNet. In this paper we briefly show how IMAGEnet uses AmazonMechanical Turk. And since the largest (public) records of tens of thousands of classes of image, as well as the current state-of-the-art computer vision algorithms for the problem of classifying large images? Then I discuss a new display screen as a "bank objects" that a significant departure from all previous imaging techniques such as bag-of-Words model with low (SIFT, HOG, GIST, etc..) We have shown that the new object of the BankRepresentation can lead to a simple SVM classifier, linear higher performance in all standard data set for classification. In addition, our sponsorship of scarce algorithms efficient and scalable for large data sets and scenes show the characteristic patterns semantically significant.

Large-scale image classification: IMAGEnet and object database

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