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Loops are an important part of classic programming techniques, but are rarely used in genetic programming. This paper presents a method of using unrestricted, i.e. nesting, loops to evolve programs for image classification tasks. Contrary to many other classification methods where pre-extracted features are typically used, we perform calculations on image regions determined by the loops. Since the...
This work presents a new approach for automatic recognition of coffee crops in RSIs. The method applies an approach based on Genetic Programming (GP) to combine texture and spectral information encoded by image descriptors. Experiments show that the proposed method yields slightly better results than the traditional MaxVer approach.
The genetic programming classifier's evolution and the classified speed is quick, the real-time performance is good, the classified recognition needs the domain knowledge to be very few, it is advantageous for the promoted use. This article carries on the imagery processing to the gathering plant bark image. It uses the Gray Level Co-occurrence Matrix technique description image the texture feature...
A mechanism involving evolutionary genetic programming (GP) and the expectation maximization algorithm (EM) is proposed to generate feature functions automatically, based on the primitive features, for an image pattern recognition system on the diagnosis of the disease OPMD. Prior to the feature function generation, we introduce a novel technique of the primitive texture feature extraction, which...
Genetic programming has been applied to various types of vision tasks. This paper extends the use of this powerful problem solving method to a more complex but more common domain, video analysis. We present the methodology as well as the experiments on two video analysis tasks: segmenting texture regions and detecting moving objects. The advantages of GP in this domain can be shown by this study....
In this paper, we propose a method for classifying textures using Genetic Programming (GP). Texture features are extracted from the energy of subimages of the wavelet decomposition. The GP is then used to evolve rules, which are arithmetic combinations of energy features, to identify whether a texture image belongs to certain class. Instead of using only one rule to discriminate the samples, a set...
This paper examines the feasibility of an approach to image retrieval from a heterogeneous collection based on texture. For each texture of interest (T), a T-vs-other classifier is evolved for small n times n windows using genetic programming. The classifier is then used to segment the images in the collection. If there is a significant contiguous area of T in an image, it is considered to contain...
We present a methodology to generate textures for fashion design using genetic programming (GP). The proposed GP based scheme evolves tree representation of procedures to generate textures. We use Contrast of the generated textures/images to filter out poor textures. After filtering, the fitness value of a new texture is set as the fitness value of a cluster of (already generated) textures which is...
A dataset of 57 breast mass mammographic images, each with 22 features computed, was used in this investigation. The extracted features relate to edge-sharpness, shape, and texture. The novelty of this paper is the adaptation and application of genetic programming (GP). To refine the pool of features available to the GP classifier, we used five feature-selection methods, including three statistical...
A dataset of 57 breast mass mammographic images, each with 22 features computed, was used in this investigation. The extracted features relate to edge-sharpness, shape, and texture. The novelty of this paper is the adaptation and application of genetic programming (GP). To refine the pool of features available to the GP classifier, we used five feature-selection methods, including three statistical...
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