&lt;title&gt;Investigation of image feature extraction by a genetic algorithm&lt;/title&gt;

Brumby, Steven P.; Theiler, James; Perkins, Simon; Harvey, Neal R.; Szymański, J.; Bloch, J. J.; Mitchell, Melanie

doi:10.1117/12.367697

Cited by 51 publications

(29 citation statements)

References 8 publications

(8 reference statements)

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“…The large number of possible alleles (e.g. [10][11][12][13][14][15][16][17][18][19][20] for each feature seriously complicates the assignment in many practical situations, because the reliability of the estimated parameters is low, since a typical baseline sample consists of only 50-100 individuals. Value clustering is quite promising for binning the alleles together to improve the assignments.…”

Section: Test Problems and Resultsmentioning

confidence: 99%

“…Building on this seminal work [5], GAs were also applied to feature extraction coupled with k nearest neighbor classifiers in [9,10] and several other algorithms [11,12]. Typically new features are created through the learning of real-value weights applied to the original features [13] or through a sequence of primitive operators encoded in the chromosome [14]. Recent attribute construction algorithms successfully utilize GA for data mining tasks [15].…”

Section: Introductionmentioning

confidence: 99%

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Dimensionality Reduction via Genetic Value Clustering

Topchy¹,

Punch²

2003

Lecture Notes in Computer Science

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Abstract. Feature extraction based on evolutionary search offers new possibilities for improving classification accuracy and reducing measurement complexity in many data mining and machine learning applications. We present a family of genetic algorithms for feature synthesis through clustering of discrete attribute values. The approach uses new compact graph-based encoding for cluster representation, where size of GA search space is reduced exponentially with respect to the number of items in partitioning, as compared to original idea of Park and Song. We apply developed algorithms and study their effectiveness for DNA fingerprinting in population genetics and text categorization.

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Section: Test Problems and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dimensionality Reduction via Genetic Value Clustering

Topchy¹,

Punch²

2003

Lecture Notes in Computer Science

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“…GP has also been shown to be a key tool in image processing and feature extraction (Tackett, 1993;Brumby et al, 1999;Howard and Roberts, 1999).…”

Section: Gp In Image Processingmentioning

confidence: 99%

Hyperspectral and Spectro-Polarimetric Pixel-Level Classification Using Genetic Programming

Rauss¹

2001

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The objective force will be relying heavily on their sensors to be a combat multiplier to help improve the force's effectiveness and survivability, particularly for reconnaissance, surveillance, and target acquisition missions. Currently, fielded passive sensor systems are generally ineffective against camouflage, concealment, and deception. Their performance is also sensitive to environmental conditions. To meet future needs, several new sensor systems are being developed and evaluated. Two of these new sensors are passive systems that collect additional, measurable characteristics of light: hyperspectral (HS) systems and spectro-polarimetric (SP) systems.To fully take advantage of the information that these systems collect requires new algorithms and techniques. This report discusses why new techniques are necessary and details the development of a computer-assisted design system for the discovery of classification algorithms via a small number of sample target and background signatures. The technique is called genetic programming (GP). GP is an adaptive learning technique that automatically generates a computer program (in this work, a mathematical equation) to solve the problem it is given. This report documents work conducted primarily between September 1999 and August 2000, while the author was on a rotation at the University of Michigan under the Federated Laboratories Consortium program. The report demonstrates that GP could be a useful technique for processing HS and SP data. The experiments reported here show that by using even the simplest of operators (addition, subtraction, multiplication and division) the GP process can develop interesting and potentially useful solution equations. The results shown here are encouraging. However, many questions remain to be answered.

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“…Genes can also take input from scratch planes, but only if that scratch plane has been written to by another gene positioned earlier in the chromosome sequence. We use a notation for genes 10 that is most easily illustrated by an example: the gene [ADDP rD0 rS1 wS2] applies pixel-by-pixel addition to two input planes, read from data plane 0 and from scratch plane 1, and writes its output to scratch plane 2. Any additional required operator parameters are listed after the output arguments.…”

Section: Representation Of Image-processing Algorithmsmentioning

confidence: 99%

<title>Evolving forest fire burn severity classification algorithms for multispectral imagery</title>

et al. 2001

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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.

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<title>Investigation of image feature extraction by a genetic algorithm</title>

Cited by 51 publications

References 8 publications

Dimensionality Reduction via Genetic Value Clustering

Dimensionality Reduction via Genetic Value Clustering

Hyperspectral and Spectro-Polarimetric Pixel-Level Classification Using Genetic Programming

<title>Evolving forest fire burn severity classification algorithms for multispectral imagery</title>

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