Target detection in SAR imagery by genetic programming

Howard, Daniel; Roberts, Simon C.; Brankin, Richard

doi:10.1016/s0965-9978(98)00093-3

Cited by 97 publications

(53 citation statements)

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“…Trujillo and Olague [31] have also used GP to generate feature extractors for computer vision applications. In addition, a GP-based detector was proposed by Howard et al [7] for detecting ship wakes in synthetic aperture radar (SAR) images. Inspired by the successful applications mentioned above, in this paper we propose evolving a spatio-temporal descriptor for human-action recognition using GP.…”

Section: Related Workmentioning

confidence: 99%

Genetic Programming-Evolved Spatio-Temporal Descriptor for Human Action Recognition

Liu¹,

Shao²,

Rockett³

2012

Procedings of the British Machine Vision Conference 2012

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The potential value of human action recognition has led to it becoming one of the most active research subjects in computer vision. In this paper, we propose a novel method to automatically generate low-level spatio-temporal descriptors showing good performance, for high-level human-action recognition tasks. We address this as an optimization problem using genetic programming (GP), an evolutionary method, which produces the descriptor by combining a set of primitive 3D operators. As far as we are aware, this is the first report of using GP for evolving spatio-temporal descriptors for action recognition. In our evolutionary architecture, the average cross-validation classification error calculated using the support-vector machine (SVM) classifier is used as the GP fitness function. We run GP on a mixed dataset combining the KTH and the Weizmann datasets to obtain a promising feature-descriptor solution for action recognition. To demonstrate generalizability, the best descriptor generated so far by GP has also been tested on the IXMAS dataset leading to better accuracies compared with some previous hand-crafted descriptors.

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Section: Related Workmentioning

confidence: 99%

Genetic Programming-Evolved Spatio-Temporal Descriptor for Human Action Recognition

Liu¹,

Shao²,

Rockett³

2012

Procedings of the British Machine Vision Conference 2012

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“…These include different kind of neural networks [30,28,31,32], genetic algorithms [33,34], decision trees [35], statistical methods such as Gaussian models and Naive Bayes [36,35], support vector machines [36,35], genetic programming [13,37,22,38], and hybrid methods [39,40,41].…”

Section: Related Workmentioning

confidence: 99%

“…For the simple binary classification case, this translation can be based on the sign of the numeric value [8,7,9,13,14,15,16]; for multiclass problems, finding the appropriate boundary values to separate the different classes is more difficult.…”

Section: Introductionmentioning

confidence: 99%

Using Gaussian distribution to construct fitness functions in genetic programming for multiclass object classification

Zhang

Smart

2006

Pattern Recognition Letters

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This paper describes a new approach to the use of Gaussian distribution in genetic programming (GP) for multiclass object classification problems. Instead of using predefined multiple thresholds to form different regions in the program output space for different classes, this approach uses probabilities of different classes, derived from Gaussian distributions, to construct the fitness function for classification. Two fitness measures, overlap area and weighted distribution distance, have been developed. Rather than using the best evolved program in a population, this approach uses multiple programs and a voting strategy to perform classification. The approach is examined on three multiclass object classification problems of increasing difficulty and compared with a basic GP approach. The results suggest that the new approach is more effective and more efficient than the basic GP approach. Although developed for object classification, this approach is expected to be able to be applied to other classification problems.Keywords Probability based genetic programming, object recognition, object detection, fitness function, multiclass classification Author InformationThe first author is a staff member in computer science, Victoria University of Wellington, New Zealand. The second author is postgraduate student in computer science, Victoria University of Wellington, New Zealand. Using Gaussian Distribution to Construct Fitness Functions in Genetic Programming AbstractThis paper describes a new approach to the use of Gaussian distribution in genetic programming (GP) for multiclass object classification problems. Instead of using predefined multiple thresholds to form different regions in the program output space for different classes, this approach uses probabilities of different classes, derived from Gaussian distributions, to construct the fitness function for classification. Two fitness measures, overlap area and weighted distribution distance, have been developed. Rather than using the best evolved program in a population, this approach uses multiple programs and a voting strategy to perform classification. The approach is examined on three multiclass object classification problems of increasing difficulty and compared with a basic GP approach. The results suggest that the new approach is more effective and more efficient than the basic GP approach. Although developed for object classification, this approach is expected to be able to be applied to other classification problems.

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“…Bhanu and Lin [2] use GP for object detection and ROI extraction. Howard et al [3] apply GP for automatic detection of ships in low resolution SAR imagery. Roberts and Howard [4] use GP to develop automatic object detectors in infrared images.…”

Section: Motivation and Related Researchmentioning

confidence: 99%

Learning Features for Object Recognition

Lin

Bhanu

2003

Genetic and Evolutionary Computation — GECCO 2003

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Abstract. Features represent the characteristics of objects and selecting or synthesizing effective composite features are the key factors to the performance of object recognition. In this paper, we propose a co-evolutionary genetic programming (CGP) approach to learn composite features for object recognition. The motivation for using CGP is to overcome the limitations of human experts who consider only a small number of conventional combinations of primitive features during synthesis. On the other hand, CGP can try a very large number of unconventional combinations and these unconventional combinations may yield exceptionally good results in some cases. Our experimental results with real synthetic aperture radar (SAR) images show that CGP can learn good composite features. We show results to distinguish objects from clutter and to distinguish objects that belong to several classes.

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Target detection in SAR imagery by genetic programming

Cited by 97 publications

References 1 publication

Genetic Programming-Evolved Spatio-Temporal Descriptor for Human Action Recognition

Genetic Programming-Evolved Spatio-Temporal Descriptor for Human Action Recognition

Using Gaussian distribution to construct fitness functions in genetic programming for multiclass object classification

Learning Features for Object Recognition

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