Simon J. Perkins

Accurate on-line support vector regression

Batch implementations of support vector regression (SVR) are inefficient when used in an on-line setting because they must be retrained from scratch every time the training set is modified. Following an incremental support vector classification algorithm introduced by Cauwenberghs and Poggio (2001), we have developed an accurate on-line support vector regression (AOSVR) that efficiently updates a trained SVR function whenever a sample is added to or removed from the training set. The updated SVR function is identical to that produced by a batch algorithm. Applications of AOSVR in both on-line and cross-validation scenarios are presented.Inbothscenarios, numerical experiments indicate that AOSVR is faster than batch SVR algorithms with both cold and warm start.

Online novelty detection on temporal sequences

In this paper, we present a new framework for online novelty detection on temporal sequences. This framework include a mechanism for associating each detection result with a confidence value. Based on this framework, we develop a concrete online detection algorithm, by modeling the temporal sequence using an online support vector regression algorithm. Experiments on both synthetic and real world data are performed to demonstrate the promising performance of our proposed detection algorithm.

Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction

The authors have developed an automated feature detection/classification system, called GENetic Imagery Exploitation (GENIE), which has been designed to generate image processing pipelines for a variety of feature detection/classification tasks. GENIE is a hybrid evolutionary algorithm that addresses the general problem of finding features of interest in multispectral remotely-sensed images. The authors describe their system in detail together with experiments involving comparisons of GENIE with several conventional supervised classification techniques, for a number of classification tasks using multispectral remotely sensed imagery.

Genetic algorithms and support vector machines for time series classification

We introduce an algorithm for classifying time series data. Since our initial application is for lightning data, we call the algorithm Zeus. Zeus is a hybrid algorithm that employs evolutionary computation for feature extraction, and a support vector machine for the final backend classification. Support vector machines have a reputation for classifying in high-dimensional spaces without overfitting, so the utility of reducing dimensionality with an intermediate feature selection step has been questioned. We address this question by testing Zeus on a lightning classification task using data acquired from the Fast On-orbit Recording of Transient Events (FORTE) satellite.

USING BLOCKS OF SKEWERS FOR FASTER COMPUTATION OF PIXEL PURITY INDEX

The “pixel purity index” (PPI) algorithm proposed by Boardman, et al1 identifies potential endmember pixels in multispectral imagery. The algorithm generates a large number of “skewers” (unit vectors in random directions), and then computes the dot product of each skewer with each pixel. The PPI is incremented for those pixels associated with the extreme values of the dot products. A small number of pixels (a subset of those with the largest PPI values) are selected as “pure” and the rest of the pixels in the image are expressed as linear mixtures of these pure endmembers. This provides a convenient and physically-motivated decomposition of the image in terms of a relatively few components. We report on a variant of the PPI algorithm in which blocks of B skewers are considered at a time. Prom the computation of B dot products, one can produce a much larger set of “derived” dot products that are associated with skewers that are linear combinations of the original B skewers. Since the derived dot products involve only scalar operations, instead of full vector dot products, they can be very cheaply computed. We will also discuss a hardware implementation on a field programmable gate array (FPGA) processor both of the original PPI algorithm and of the block-skewer approach. We will furthermore discuss the use of fast PPI as a front-end to more sophisticated algorithms for selecting the actual endmembers.

Finding Golf Courses: The Ultra High Tech Approach

The search for a suitable golf course is a very important issue in the travel plans of any modern manager. Modern management is also infamous for its penchant for high-tech gadgetry. Here we combine these two facets of modern management life. We aim to provide the cutting-edge manager with a method of finding golf courses from space! In this paper, we present GENIE: a hybrid evolutionary algorithm-based system that tackles the general problem of finding features of interest in multi-spectral remotely-sensed images, including, but not limited to, golf courses. Using this system we are able to successfully locate golf courses in 10-channel satellite images of several desirable US locations.

Genetic algorithm for combining new and existing image processing tools for multispectral imagery

We describe the implementation and performance of a genetic algorithm (GA) which evolves and combines image processing tools for multispectral imagery (MSI) datasets. Existing algorithms for particular features can also be “re-tuned” and combined with the newly evolved image processing tools to rapidly produce customized feature extraction tools. First results from our software system were presented previously. We now report on work extending our system to look for a range of broad-area features in MSI datasets. These features demand an integrated spatio- spectral approach, which our system is designed to use. We describe our chromosomal representation of candidate image processing algorithms, and discuss our set of image operators. Our application has been geospatial feature extraction using publicly available MSI and hyperspectral imagery (HSI). We demonstrate our system on NASA/Jet Propulsion Laboratory’s Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) HSI which has been processed to simulate MSI data from the Department of Energy’s Multispectral Thermal Imager (MTI) instrument. We exhibit some of our evolved algorithms, and discuss their operation and performance.

Investigation of image feature extraction by a genetic algorithm

We describe the implementation and performance of a genetic algorithm which generates image feature extraction algorithms for remote sensing applications. We describe our basis set of primitive image operators and present our chromosomal representation of a complete algorithm. Our initial application has been geospatial feature extraction using publicly available multi-spectral aerial-photography data sets. We present the preliminary results of our analysis of the efficiency of the classic genetic operations of crossover and mutation for our application, and discuss our choice of evolutionary control parameters. We exhibit some of our evolved algorithms, and discuss possible avenues for future progress.

Evolving forest fire burn severity classification algorithms for multi-spectral imagery

Between May 6 and May 18, 2000, the Cerro Grande/Los Alamos wildfire burned approximately 43,000 acres (17,500 ha) and 235 residences in the town of Los Alamos, NM. Initial estimates of forest damage included 17,000 acres (6,900 ha) of 70-100% tree mortality. Restoration efforts following the fire were complicated by the large scale of the fire, and by the presence of extensive natural and man-made hazards. These conditions forced a reliance on remote sensing techniques for mapping and classifying the burn region. During and after the fire, remote-sensing data was acquired from a variety of aircraft-based and satellite-based sensors, including Landsat 7. We now report on the application of a machine learning technique, implemented in a software package called GENIE, to the classification of forest fire burn severity using Landsat 7 ETM+ multispectral imagery. The details of this automatic classification are compared to the manually produced burn classification, which was derived from field observations and manual interpretation of high-resolution aerial color/infrared photography.

Genie Pro: robust image classification using shape, texture, and spectral information

We present Genie Pro, a new software tool for image analysis produced by the ISIS (Intelligent Search in Images and Signals) group at Los Alamos National Laboratory. Like the earlier GENIE tool produced by the same group, Genie Pro is a general purpose adaptive tool that derives automatic pixel classification algorithms for satellite/aerial imagery, from training input provided by a human expert. Genie Pro is a complete rewrite of our earlier work that incorporates many new ideas and concepts. In particular, the new software integrates spectral information; and spatial cues such as texture, local morphology and large-scale shape information; in a much more sophisticated way. In addition, attention has been paid to how the human expert interacts with the software: Genie Pro facilitates highly efficient training through an interactive and iterative “training dialog”. Finally, the new software runs on both Linux and Windows platforms, increasing its versatility. We give detailed descriptions of the new techniques and ideas in Genie Pro, and summarize the results of a recent evaluation of the software.