Genetic algorithms for scene interpretation from prototypical semantic description

Prabhu, D.; Buckles, Bill P.; Petry, Frederick E.

doi:10.1002/1098-111x(200010)15:10<901::aid-int1>3.0.co;2-c

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…The Red Green Blue (RGB) is the space representation of images that we used. In this case, and as the first step in designing an evolutionary algorithm is to encode (represent) solutions as chromosomes which are strings of genes mainly depends on the considered application and the mannered goal [11], then an encoding of individuals is thus introduced.…”

Section: Encodingmentioning

confidence: 99%

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Images encryption by the use of evolutionary algorithms

Souici

Séridi

Akdag

2011

Analog Integr Circ Sig Process

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Increasing information transmission in public networks raises a significant number of questions. For example, the security, the confidentiality, the integrity and the authenticity of the data during its transmission are very problematical. So, encryption of the transmitted data is one of the most promising solutions. In our work, we focus on the security of image data, which are considered as specific data because of their big size and their information which are of two-dimensional nature and also redundant. These data characteristics make the developed algorithms in the literature unavailable in their classical forms, because of the speed and the possible risk of information loss. In this paper, we develop an original ''images encryption'' algorithm based on evolutionary algorithms. The appropriateness of the proposed scheme is demonstrated by the sensitivity to images, the key and the resistibility to various advanced attacks.

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Section: Encodingmentioning

confidence: 99%

“…So, in order to evaluate or quantify the difference between two images, we used the same measures as those used in [28]. The Number of Pixels Change Rate (NPCR) formulated through the expression (9), Mean Absolute Error (MAE) given by the expression (11) and Mean Square Error (MSE) given by the expression (12). …”

Section: Statistical Attackmentioning

confidence: 99%

Images encryption by the use of evolutionary algorithms

Souici

Séridi

Akdag

2011

Analog Integr Circ Sig Process

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“…The overall GA optimization system [5] is described by the schematic in Figure 1. GA starts with an initial population of coded strings, which are generally called individual or chromosome and randomly selected.…”

Section: Standard Genetic Algorithmmentioning

confidence: 99%

A genetic algorithm for generating test from a question bank

Yildirim

2010

Comp Applic In Engineering

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The purpose of this study is to provide academicians with efficient means of generating tests with multiple-choice questions from a question bank. Genetic algorithm (GA) is used to optimize predefined criteria for selecting questions from the question bank. GA is a very useful optimization algorithm because of its versatility. However, crossover and mutation operator of standard GA cannot be directly usable for generating test, since integer-coded individuals have to be used and these operators produce duplicated genoms on individuals. In this study, a mutation operation is proposed for preventing the duplications on crossovered individuals. The experiments and analysis show that GA with proposed mutation operator is successful as approximately 100%. ß

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“…The EA-based approach to automatic classification and labeling of the objects observed in the image is further developed by Buckles and his co-workers [16,17], and applied to the classification of noisy and obscured natural scenes from oceanographic and meteorological data collection. Investigators report that a series of experiments on the identification of ocean currents in infrared imagery of North Atlantic, and on the classification of clouds in AVHRR (Advanced Very High Resolution Radiometer) imagery of the Western US show a high degree of accuracy.…”

Section: Semantic Scene Interpretationmentioning

confidence: 99%