Showing papers by "Rob Fergus published in 2004"

Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories

Abstract: Current computational approaches to learning visual object categories require thousands of training images, are slow, cannot learn in an incremental manner and cannot incorporate prior information into the learning process. In addition, no algorithm presented in the literature has been tested on more than a handful of object categories. We present an method for learning object categories from just a few training images. It is quick and it uses prior information in a principled way. We test it on a dataset composed of images of objects belonging to 101 widely varied categories. Our proposed method is based on making use of prior information, assembled from (unrelated) object categories which were previously learnt. A generative probabilistic model is used, which represents the shape and appearance of a constellation of features belonging to the object. The parameters of the model are learnt incrementally in a Bayesian manner. Our incremental algorithm is compared experimentally to an earlier batch Bayesian algorithm, as well as to one based on maximum-likelihood. The incremental and batch versions have comparable classification performance on small training sets, but incremental learning is significantly faster, making real-time learning feasible. Both Bayesian methods outperform maximum likelihood on small training sets.

A Visual Category Filter for Google Images

Abstract: We extend the constellation model to include heterogeneous parts which may represent either the appearance or the geometry of a region of the object. The parts and their spatial configuration are learnt simultaneously and automatically, without supervision, from cluttered images.

Method and apparatus for retrieving visual object categories from a database containing images

Abstract: A method and apparatus for determining the relevance of images retrieved from a database relative to a specified visual object category. The method comprises transforming a visual object category into a model defining features of the visual object category and a spatial relationship therebetween, storing the model, comparing a set of images identified during the database search with the stored model, calculating a likelihood value relating to each image based on its correspondence with the model, and ranking the images in order of the respective likelihood values. The apparatus comprises a processor for transforming a visual object category into a model defining features of the visual object category and a spatial relationship therebetween.

Sampling Methods for Unsupervised Learning

Abstract: We present an algorithm to overcome the local maxima problem in estimating the parameters of mixture models. It combines existing approaches from both EM and a robust fitting algorithm, RANSAC, to give a data-driven stochastic learning scheme. Minimal subsets of data points, sufficient to constrain the parameters of the model, are drawn from proposal densities to discover new regions of high likelihood. The proposal densities are learnt using EM and bias the sampling toward promising solutions. The algorithm is computationally efficient, as well as effective at escaping from local maxima. We compare it with alternative methods, including EM and RANSAC, on both challenging synthetic data and the computer vision problem of alpha-matting.