Google Inc.

Columbia University

Jun Wang

Sanjiv Kumar

Shih-Fu Chang

Paul Sajda

David Jangraw

Eric Pohlmeyer

Barbara Hanna

Human visual perception is able to recognize a wide range of targets but has limited throughput. Machine vision can process images at a high speed but suffers from inadequate recognition accuracy of general target classes. Systems and methods are provided that combine the strengths of both systems and improve upon existing multimedia processing systems and methods to provide enhanced multimedia labeling, categorization, searching, and navigation.

Rapid image annotation via brain state decoding and visual pattern mining

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating hash functions. In one aspect, a method includes generating hash functions by sequentially determining a weight vector for each hash function to maximize an accuracy measure derived from current constraint weights and updating the current constraint weights for use in calculating a weight vector of a next hash function in the sequence. In another aspect, the determined weight vector maximizes an accuracy measure and a variance measure. In still another aspect, a method includes generating an adjusted covariance matrix and generating a sequence of hash functions from the adjusted covariance matrix. In still another aspect, a method includes sequentially generating a sequence of hash functions, where the weight vectors for any previously generated hash functions are used to identify constraints used to generate the weight vector for each next hash function in the sequence.

Semi-supervised and unsupervised generation of hash functions

Growing community of inventors

New City, NY, United States of America

Jun Wang