Author name cluster

Paul Viola

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17 papers

2 author rows

NeurIPS Conference 2007 Conference Paper

Multiple-Instance Pruning For Learning Efficient Cascade Detectors

Cha Zhang
Paul Viola

Cascade detectors have been shown to operate extremely rapidly, with high accuracy, and have important applications such as face detection. Driven by this success, cascade earning has been an area of active research in recent years. Nevertheless, there are still challenging technical problems during the training process of cascade detectors. In particular, determining the optimal target detection rate for each stage of the cascade remains an unsolved issue. In this paper, we propose the multiple instance pruning (MIP) algorithm for soft cascades. This algorithm computes a set of thresholds which aggressively terminate computation with no reduction in detection rate or increase in false positive rate on the training dataset. The algorithm is based on two key insights: i) examples that are destined to be rejected by the complete classifier can be safely pruned early; ii) face detection is a multiple instance learning problem. The MIP process is fully automatic and requires no assumptions of probability distributions, statistical independence, or ad hoc intermediate rejection targets. Experimental results on the MIT+CMU dataset demonstrate significant performance advantages.

AIJ Journal 2006 Journal Article

Corrective feedback and persistent learning for information extraction

Aron Culotta
Trausti Kristjansson
Andrew McCallum
Paul Viola

To successfully embed statistical machine learning models in real world applications, two post-deployment capabilities must be provided: (1) the ability to solicit user corrections and (2) the ability to update the model from these corrections. We refer to the former capability as corrective feedback and the latter as persistent learning. While these capabilities have a natural implementation for simple classification tasks such as spam filtering, we argue that a more careful design is required for structured classification tasks. One example of a structured classification task is information extraction, in which raw text is analyzed to automatically populate a database. In this work, we augment a probabilistic information extraction system with corrective feedback and persistent learning components to assist the user in building, correcting, and updating the extraction model. We describe methods of guiding the user to incorrect predictions, suggesting the most informative fields to correct, and incorporating corrections into the inference algorithm. We also present an active learning framework that minimizes not only how many examples a user must label, but also how difficult each example is to label. We empirically validate each of the technical components in simulation and quantify the user effort saved. We conclude that more efficient corrective feedback mechanisms lead to more effective persistent learning.

NeurIPS Conference 2005 Conference Paper

Multiple Instance Boosting for Object Detection

Cha Zhang
John Platt
Paul Viola

A good image object detection algorithm is accurate, fast, and does not require exact locations of objects in a training set. We can create such an object detector by taking the architecture of the Viola-Jones detector cascade and training it with a new variant of boosting that we call MILBoost. MILBoost uses cost functions from the Multiple Instance Learning literature combined with the AnyBoost framework. We adapt the feature selection criterion of MILBoost to optimize the performance of the Viola-Jones cascade. Experiments show that the detection rate is up to 1. 6 times better using MILBoost. This increased detection rate shows the advantage of simultaneously learning the locations and scales of the objects in the training set along with the parameters of the classifier.

AAAI Conference 2004 Conference Paper

Interactive Information Extraction with Constrained Conditional Random Fields

Trausti Kristjansson
Paul Viola

Information Extraction methods can be used to automatically “fill-in” database forms from unstructured data such as Web documents or email. State-of-the-art methods have achieved low error rates but invariably make a number of errors. The goal of an interactive information extraction system is to assist the user in filling in database fields while giving the user confidence in the integrity of the data. The user is presented with an interactive interface that allows both the rapid verification of automatic field assignments and the correction of errors. In cases where there are multiple errors, our system takes into account user corrections, and immediately propagates these constraints such that other fields are often corrected automatically. Linear-chain conditional random fields (CRFs) have been shown to perform well for information extraction and other language modelling tasks due to their ability to capture arbitrary, overlapping features of the input in a Markov model. We apply this framework with two extensions: a constrained Viterbi decoding which finds the optimal field assignments consistent with the fields explicitly specified or corrected by the user; and a mechanism for estimating the confidence of each extracted field, so that low-confidence extractions can be highlighted. Both of these mechanisms are incorporated in a novel user interface for form filling that is intuitive and speeds the entry of data—providing a 23% reduction in error due to automated corrections.

NeurIPS Conference 2001 Conference Paper

Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade

Paul Viola
Michael Jones

This paper develops a new approach for extremely fast detection in do- mains where the distribution of positive and negative examples is highly skewed (e. g. face detection or database retrieval). In such domains a cascade of simple classiﬁers each trained to achieve high detection rates and modest false positive rates can yield a ﬁnal detector with many desir- able features: including high detection rates, very low false positive rates, and fast performance. Achieving extremely high detection rates, rather than low error, is not a task typically addressed by machine learning al- gorithms. We propose a new variant of AdaBoost as a mechanism for training the simple classiﬁers used in the cascade. Experimental results in the domain of face detection show the training algorithm yields sig- niﬁcant improvements in performance over conventional AdaBoost. The ﬁnal face detection system can process 15 frames per second, achieves over 90% detection, and a false positive rate of 1 in a 1, 000, 000.

NeurIPS Conference 2000 Conference Paper

Learning Joint Statistical Models for Audio-Visual Fusion and Segregation

John Fisher III
Trevor Darrell
William Freeman
Paul Viola

People can understand complex auditory and visual information, often using one to disambiguate the other. Automated analysis, even at a low(cid: 173) level, faces severe challenges, including the lack of accurate statistical models for the signals, and their high-dimensionality and varied sam(cid: 173) pling rates. Previous approaches [6] assumed simple parametric models for the joint distribution which, while tractable, cannot capture the com(cid: 173) plex signal relationships. We learn the joint distribution of the visual and auditory signals using a non-parametric approach. First, we project the data into a maximally informative, low-dimensional subspace, suitable for density estimation. We then model the complicated stochastic rela(cid: 173) tionships between the signals using a nonparametric density estimator. These learned densities allow processing across signal modalities. We demonstrate, on synthetic and real signals, localization in video of the face that is speaking in audio, and, conversely, audio enhancement of a particular speaker selected from the video.

NeurIPS Conference 1999 Conference Paper

Learning Informative Statistics: A Nonparametnic Approach

John Fisher III
Alexander Ihler
Paul Viola

We discuss an information theoretic approach for categorizing and mod(cid: 173) eling dynamic processes. The approach can learn a compact and informa(cid: 173) tive statistic which summarizes past states to predict future observations. Furthermore, the uncertainty of the prediction is characterized nonpara(cid: 173) metrically by a joint density over the learned statistic and present obser(cid: 173) vation. We discuss the application of the technique to both noise driven dynamical systems and random processes sampled from a density which is conditioned on the past. In the first case we show results in which both the dynamics of random walk and the statistics of the driving noise are captured. In the second case we present results in which a summarizing statistic is learned on noisy random telegraph waves with differing de(cid: 173) pendencies on past states. In both cases the algorithm yields a principled approach for discriminating processes with differing dynamics and/or de(cid: 173) pendencies. The method is grounded in ideas from information theory and nonparametric statistics.

NeurIPS Conference 1998 Conference Paper

Restructuring Sparse High Dimensional Data for Effective Retrieval

Charles Isbell
Paul Viola

The task in text retrieval is to find the subset of a collection of documents relevant to a user's information request, usually expressed as a set of words. Classically, documents and queries are represented as vectors of word counts. In its simplest form, relevance is defined to be the dot product between a document and a query vector-a measure of the number of common terms. A central difficulty in text retrieval is that the presence or absence of a word is not sufficient to determine relevance to a query. Linear dimensionality reduction has been proposed as a tech(cid: 173) nique for extracting underlying structure from the document collection. In some domains (such as vision) dimensionality reduction reduces computational com(cid: 173) plexity. In text retrieval it is more often used to improve retrieval performance. We propose an alternative and novel technique that produces sparse represen(cid: 173) tations constructed from sets of highly-related words. Documents and queries are represented by their distance to these sets, and relevance is measured by the number of common clusters. This technique significantly improves retrieval per(cid: 173) formance, is efficient to compute and shares properties with the optimal linear projection operator and the independent components of documents.

NeurIPS Conference 1997 Conference Paper

A Non-Parametric Multi-Scale Statistical Model for Natural Images

Jeremy De Bonet
Paul Viola

The observed distribution of natural images is far from uniform. On the contrary, real images have complex and important struc(cid: 173) ture that can be exploited for image processing, recognition and analysis. There have been many proposed approaches to the prin(cid: 173) cipled statistical modeling of images, but each has been limited in either the complexity of the models or the complexity of the im(cid: 173) ages. We present a non-parametric multi-scale statistical model for images that can be used for recognition, image de-noising, and in a "generative mode" to synthesize high quality textures.

NeurIPS Conference 1997 Conference Paper

Bayesian Model of Surface Perception

William Freeman
Paul Viola

Image intensity variations can result from several different object surface effects, including shading from 3-dimensional relief of the object, or paint on the surface itself. An essential problem in vision, which people solve naturally, is to attribute the proper physical cause, e. g. surface relief or paint, to an observed image. We ad(cid: 173) dressed this problem with an approach combining psychophysical and Bayesian computational methods. We assessed human performance on a set of test images, and found that people made fairly consistent judgements of surface properties. Our computational model assigned simple prior probabilities to different relief or paint explanations for an image, and solved for the most probable interpretation in a Bayesian framework. The ratings of the test images by our algorithm compared surprisingly well with the mean ratings of our subjects.

NeurIPS Conference 1997 Conference Paper

Structure Driven Image Database Retrieval

Jeremy De Bonet
Paul Viola

A new algorithm is presented which approximates the perceived visual similarity between images. The images are initially trans(cid: 173) formed into a feature space which captures visual structure, tex(cid: 173) ture and color using a tree of filters. Similarity is the inverse of the distance in this perceptual feature space. Using this algorithm we have constructed an image database system which can perform example based retrieval on large image databases. Using carefully constructed target sets, which limit variation to only a single visual characteristic, retrieval rates are quantitatively compared to those of standard methods.

NeurIPS Conference 1996 Conference Paper

MIMIC: Finding Optima by Estimating Probability Densities

Jeremy De Bonet
Charles Isbell
Paul Viola

In many optimization problems, the structure of solutions reflects complex relationships between the different input parameters. For example, experience may tell us that certain parameters are closely related and should not be explored independently. Similarly, ex(cid: 173) perience may establish that a subset of parameters must take on particular values. Any search of the cost landscape should take advantage of these relationships. We present MIMIC, a framework in which we analyze the global structure of the optimization land(cid: 173) scape. A novel and efficient algorithm for the estimation of this structure is derived. We use knowledge of this structure to guide a randomized search through the solution space and, in turn, to re(cid: 173) fine our estimate ofthe structure. Our technique obtains significant speed gains over other randomized optimization procedures.

NeurIPS Conference 1995 Conference Paper

Classifying Facial Action

Marian Bartlett
Paul Viola
Terrence Sejnowski
Beatrice Golomb
Jan Larsen
Joseph Hager
Paul Ekman

The Facial Action Coding System, (FACS), devised by Ekman and Friesen (1978), provides an objective meanS for measuring the facial muscle contractions involved in a facial expression. In this paper, we approach automated facial expression analysis by detecting and classifying facial actions. We generated a database of over 1100 image sequences of 24 subjects performing over 150 distinct facial actions or action combinations. We compare three different ap(cid: 173) proaches to classifying the facial actions in these images: Holistic spatial analysis based on principal components of graylevel images; explicit measurement of local image features such as wrinkles; and template matching with motion flow fields. On a dataset contain(cid: 173) ing six individual actions and 20 subjects, these methods had 89%, 57%, and 85% performances respectively for generalization to novel subjects. When combined, performance improved to 92%.

NeurIPS Conference 1995 Conference Paper

Empirical Entropy Manipulation for Real-World Problems

Paul Viola
Nicol Schraudolph
Terrence Sejnowski

No finite sample is sufficient to determine the density, and therefore the entropy, of a signal directly. Some assumption about either the functional form of the density or about its smoothness is necessary. Both amount to a prior over the space of possible density functions. By far the most common approach is to assume that the density has a parametric form. By contrast we derive a differential learning rule called EMMA that optimizes entropy by way of kernel density estimation. En(cid: 173) tropy and its derivative can then be calculated by sampling from this density estimate. The resulting parameter update rule is sur(cid: 173) prisingly simple and efficient. We will show how EMMA can be used to detect and correct cor(cid: 173) ruption in magnetic resonance images (MRI). This application is beyond the scope of existing parametric entropy models.

NeurIPS Conference 1991 Conference Paper

Recurrent Eye Tracking Network Using a Distributed Representation of Image Motion

Paul Viola
Stephen Lisberger
Terrence Sejnowski

This paper briefly describes an artificial neural network for preattentive visual processing. The network is capable of determiuing image motioll in a type of stimulus which defeats most popular methods of motion detect. ion - a subset of second-order visual motion stimuli known as drift-balanced stimuli(DBS). The processing st. ages of the network described in this paper are integratable into a model capable of simultaneous motion extractioll. edge detection, and the determination of occlusion.

ICRA Conference 1990 Conference Paper

Cooperative control of a semi-autonomous mobile robot

Jonathan Connell
Paul Viola

Multiagent control systems for robots are considered. A robot is described that is based on treating the existing behavioral agents as simply an enhanced effector command language and designing a higher-level control structure that switches them on and off. It is shown how supervisory control can be added to such a reactive multiagent system. >

NeurIPS Conference 1989 Conference Paper

Neurally Inspired Plasticity in Oculomotor Processes

Paul Viola

We have constructed a two axis camera positioning system which is roughly analogous to a single human eye. This Artificial-Eye (A(cid: 173) eye) combines the signals generated by two rate gyroscopes with motion information extracted from visual analysis to stabilize its camera. This stabilization process is similar to the vestibulo-ocular response (VOR); like the VOR, A-eye learns a system model that can be incrementally modified to adapt to changes in its structure, performance and environment. A-eye is an example of a robust sen(cid: 173) sory system that performs computations that can be of significant use to the designers of mobile robots.