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ABSTRACT

With the advent of the Internet, billions of images are now freely available online and constitute a dense sampling of the visual world. Using a variety of non?parametric methods, we explore this world with the aid of a large dataset of 79,302,017 images collected from the Web. Motivated by psychophysical results showing the remarkable tolerance of the human visual system to degradations in image resolution, the images in the dataset are stored as 32×32 color images. Each image is loosely labeled with one of the 75,062 non?abstract nouns in English, as listed in the Wordnet lexical database. Hence the image database gives a comprehensive coverage of all object categories and scenes. The semantic information from Wordnet can be used in conjunction with nearest?neighbor methods to perform object classification over a range of semantic levels minimizing the effects of labeling noise. For certain classes that are particularly prevalent in the dataset, such as people, we are able to demonstrate a recognition performance comparable to class?specific Viola?Jones style detectors.

In the second part of the talk, we present efficient image search and scene matching techniques that are not only fast, but also require very little memory, enabling their use on standard hardware or even on handheld devices. Our approach uses the Semantic Hashing idea of Salakhutdinov and Hinton, based on Restricted Boltzmann Machines to convert the Gist descriptor (a real valued vector that describes orientation energies at different scales and orientations within an image) to a compact binary code, with a few hundred bits per image. Using our scheme, it is possible to perform real?time searches on our Internet image database using a single large PC and obtain recognition results comparable to the full descriptor. Using our codes on high quality labeled images from the LabelMe database gives surprisingly powerful recognition results using simple nearest neighbor techniques.

This talk will be taped

Speaker: Rob Fergus
Rob Fergus is an istant Professor of Computer Science at the Courant Institute of Mathematical Sciences, New York University. Originally from the UK, he has a undergraduate degree in Electrical Engineering from the University of Cambridge. He then did a Masters in Electrical Engineering with Prof. Pietro Perona at Caltech, before completing a PhD with Prof. Andrew Zisserman at the University of Oxford. Before coming to NYU, he spent two years as a post-doc in the Computer Science and Artificial Intelligence Lab (CSAIL) at MIT, working with Prof. William Freeman.

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ABSTRACT

The Semantic Web presents the vision of a distributed, dynamically growing knowledge base founded on formal logic. Common users, however, seem to have problems even with the simplest Boolean
expression. So how can we help users to query a web of logic that they do not seem to understand? One frequently proposed solution to address this problem is the use of natural language (NL) for
knowledge specification and querying. We propose to regard formal query languages and NL as two extremes of a continuum, where semistructured languages lie somewhere in the middle.

To evaluate what degree of structuredness casual users prefer, we introduce four query interfaces, each at a different point in the continuum, and evaluate the users’ preference and their query performance in a study with 48 subjects. The results of the study reveal that while the users dislike the constraints of a fully
structured formal query language they also seem at a loss with the freedom of a full NLP approach. This suggests that restricted query languages will be preferred by casual users because of their
guidance effect, mirroring findings from social science theory on human activity in general.

Speaker: Prof. Bernstein
Abraham Bernstein is a full Professor at the Department of Information Technology (Institut für Informatik) of the University of Zurich. He conducts research on various aspects of supporting dynamic (intra- and inter-) organizational processes. His work draws from both social science (organizational psychology/sociology) and technical (computer science, artificial intelligence) foundations.

Before coming to Zurich he was an istant Professor, at the Information Systems Department in New York University’s Stern School of Business, and received a Ph.D. at MIT’s Sloan School of Management, where he worked with Prof. Thomas W. Malone at the Center for Coordination Science.

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