Automatic texture feature selection for image pixel classification

Puig, Domènec; García, Miguel Ángel

doi:10.1016/j.patcog.2006.05.016

Cited by 36 publications

(29 citation statements)

References 14 publications

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“…As renowned search and optimization methods, GAs have been successfully applied to a wide range of image processing problems, such as image classification [77][78][79], image segmentation [80], feature extraction [81][82][83] and inverse modeling [84,85]. The GA is a technique that uses genetic evolution as a pattern for problem solving.…”

Section: Feature Selection By the Gamentioning

confidence: 99%

Designing a New Framework Using Type-2 FLS and Cooperative-Competitive Genetic Algorithms for Road Detection from IKONOS Satellite Imagery

et al. 2015

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Abstract:The growing availability of high-resolution satellite imagery provides an opportunity for identifying road objects. Most studies associated with road detection are scene-related and also based on the digital number of each pixel. Because images can provide more details (including color, size, shape, and texture), object-based processing is more advantageous. Therefore, in this paper, to handle the existing uncertainty of satellite image pixel values, using type-2 fuzzy set theory in combination with object-based image analysis is proposed. Because the main challenges of the type-2 fuzzy set are parameter tuning and extensive computations, a hybrid genetic algorithm (GA) consisting of Pittsburgh and cooperative-competitive learning schemes is proposed to address these problems. The most prominent feature of our research in this work is to establish a comprehensive object-based type-2 fuzzy logic system that enables us to detect roads in high-resolution satellite images with no training data. The validation assessment of road detection results using the proposed framework for independent images demonstrates the capability and efficiency of our method in identifying road objects. For more evaluation, a type-1 fuzzy logic system with the same structure as type-2 is tuned. Evaluations show that type-1 fuzzy logic system quality in training is very similar to that of the proposed type-2 fuzzy OPEN ACCESS Remote Sens. 2015, 7 8272 framework. However, in general, its lower accuracy, as inferred by validation assessments, makes the type-1 fuzzy logic system significantly different from the proposed type-2.

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Section: Feature Selection By the Gamentioning

confidence: 99%

Designing a New Framework Using Type-2 FLS and Cooperative-Competitive Genetic Algorithms for Road Detection from IKONOS Satellite Imagery

et al. 2015

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“…Many techniques may be used to capture and represent the underlying characteristics of a given image [5,11,13]. In this work, local grey level histograms and the first and second order color statistics are exploited to produce a feature pattern for each individual pixel.…”

Section: Feature Extractionmentioning

confidence: 99%

“…One critical step to successfully build an image classifier is to extract informative features from given images [5,8,10,13]. Without explicit prior knowledge of what characteristics might best represent an original image, many features may have to be extracted.…”

Section: Introductionmentioning

confidence: 99%

Effective Feature Selection for Mars McMurdo Terrain Image Classification

Shang

Barnes

Shen

2009

2009 Ninth International Conference on Intelligent Systems Design and Applications

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“…All road segmentation algorithms are mainly classified into two categories: region properties based approaches [1][2][3][4][5][6][7] and boundary based approaches [8][9][10][11]. Various road types i.e.…”

Section: Introductionmentioning

confidence: 99%