Genetic Programming for Multiple Class Object Detection

Zhang, Mengjie; Ciesielski, Victor

doi:10.1007/3-540-46695-9_16

Cited by 74 publications

(51 citation statements)

References 11 publications

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“…The overall system performance can be defined as the product of all units accuracy rates (Extraction of plate region, segmentation of characters and recognition of characters). Recognition Rate of LPR System =pi(Percentages of Accuracy) For vehicle detection in [6], a data base of pictures with their locations and class of objects were manually determined. This algorithm for vehicle identification by plate recognition uses GA.…”

Section: Resultsmentioning

confidence: 99%

Identification of Vehicle by Number Plate Recognition Using Genetic Algorithm

Nigrel¹,

Ashokan²,

Barua³

et al. 2015

International Journal of Advanced Research in Computer and Comm

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Design of an algorithm for vehicle identification by recognizing the number plate is presented. Automatic Vehicle Identification (AVI) has many applications in traffic systems (highway electronic toll collection, red light violation enforcement, border and customs checkpoints, etc.).The proposed algorithm consists of three major parts: Number plate recognition, vehicle size detection and recognition of plate characters. For extracting the plate region, edge detection algorithms and smearing algorithms are used. In segmentation part, smearing algorithms, filtering and some morphological algorithms are used. And finally genetic algorithm is used for recognition of plate characters. For vehicle size detection blob detection method is used and for theft detection number plates are matched. Experiments have been conducted with images taken from various scenes and conditions and the detection rate is found to be 92.5 %. Experiments have conducted for recognition with LPR images taken at different conditions and the recognition rate is found to be 91 %. Detection time is linear function of number of objects in the input image. Potential applications include provisioning of vehicle parking facilities and campus security system for permitting authorized vehicles into the premises.

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Section: Resultsmentioning

confidence: 99%

Identification of Vehicle by Number Plate Recognition Using Genetic Algorithm

Nigrel¹,

Ashokan²,

Barua³

et al. 2015

International Journal of Advanced Research in Computer and Comm

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“…GP research has considered a variety of kinds of classifier programs, using different program representations, including decision tree classifiers, classification rule sets [6], and linear and graph classifiers [4]. Recently, a new form of classifier representation -numeric expression (tree-like) classifiers -has been developed using GP [7,8,9,10]. This form has been successfully applied to real world classification problems such as detecting and recognising particular classes of objects in images [8,9,11,12], demonstrating the potential of GP as a general method to solve classification problems.…”

Section: Introductionmentioning

confidence: 99%

“…This includes a primary static method such as object classification map or static range selection [10,7,12], dynamic range selection [7], centred and slotted dynamic class boundary determination methods [15,16]. Past work has demonstrated the effectiveness of these approaches, particularly the dynamic methods, on a number of object classification problems.…”

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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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“…Recently, a new form of classifier representation -numeric expression classifiers -has been developed using GP [4][5][6][7]. In these years, this form has become the "standard form" of GP and has been successfully applied to some real world classification problems such as detecting and recognising particular classes of objects in images [5,6,8,9], demonstrating the potential of GP as a general method for classification problems.…”

Section: Introductionmentioning

confidence: 99%

“…For the simple binary classification case, this translation can be based on the sign of the numeric value [5,[10][11][12]; for multiclass problems, finding the appropriate boundary values to separate the different classes is more difficult. The simplest approach -fixing the boundary values at manually chosen points -often results in unnecessarily complex programs and could lead to poor performance and very long training times [4,7,9].…”

Section: Introductionmentioning

confidence: 99%

Multiclass Object Classification Using Genetic Programming

Zhang

Smart

2004

Lecture Notes in Computer Science

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We describe an approach to the use of genetic programming for multi-class object classification problems. Rather than using fixed static thresholds as boundaries to distinguish between different classes, this approach introduces two methods of classification where the boundaries between different classes can be dynamically determined during the evolutionary process. The two methods are centred dynamic class boundary determination and slotted dynamic class boundary determination. The two methods are tested on four object classification problems of increasing difficulty and are compared with the commonly used static class boundary method. The results suggest that, while the static class boundary method works well on relatively easy object classification problems, the two dynamic class boundary determination methods outperform the static method for more difficult, multiple class object classification problems.Keywords Genetic programming, genetic algorithms, dynamic class boundary determination, object recognition. Author InformationMengjie Zhang is an academic staff member in computer science and Will Smart is a postgraduate student in computer science. Both authors are in the School of Mathematical and Computing Sciences, Victoria University of Wellington, New Zealand. Multiclass Object Classification Using Genetic Programming Mengjie Zhang and Will SmartSchool of Mathematical and Computing Sciences Victoria University of Wellington, P. O. Box 600, Wellington, New Zealand, Email: {mengjie,willsmart}@mcs.vuw.ac.nz Abstract. We describe an approach to the use of genetic programming for multi-class object classification problems. Rather than using fixed static thresholds as boundaries to distinguish between different classes, this approach introduces two methods of classification where the boundaries between different classes can be dynamically determined during the evolutionary process. The two methods are centred dynamic class boundary determination and slotted dynamic class boundary determination. The two methods are tested on four object classification problems of increasing difficulty and are compared with the commonly used static class boundary method. The results suggest that, while the static class boundary method works well on relatively easy, linearly separable object classification problems, the two dynamic class boundary determination methods outperform the static method for more difficult, multiple class object classification problems.

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Genetic Programming for Multiple Class Object Detection

Cited by 74 publications

References 11 publications

Identification of Vehicle by Number Plate Recognition Using Genetic Algorithm

Identification of Vehicle by Number Plate Recognition Using Genetic Algorithm

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

Multiclass Object Classification Using Genetic Programming

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