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Contextual Pooling in Image Classification
2013The original bag-of-words (BoW) model in terms of image classification treats each local feature independently, and thus ignores the spatial relationships between a feature and its neighboring features, namely, the feature's context. However, our intuition and empirical studies tell the importance of such spatial information.
Zifeng Wu +3 more
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Contextual Possibilistic Knowledge Diffusion for Images Classification
2014In this study, an iterative contextual approach for images classification is proposed. This approach is based on the use of possibilistic reasoning in order to diffuse the possibilistic knowledge. The use of possibilistic concepts enables an important flexibility for the integration of a context-based additional semantic knowledge source formed by ...
Alsahwa, Bassem +3 more
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Fuzzy contextual classification of multisource remote sensing images
IEEE Transactions on Geoscience and Remote Sensing, 1997The authors' objective has been to model satellite image classification as a cognitive process, providing a procedure that mimics the rich interaction of human activity in solving classification problems. The key features of this approach are the definition of a knowledge-based classification methodology designed to integrate contextual information ...
Elisabetta Binaghi +3 more
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A cognitive pyramid for contextual classification of remote sensing images
IEEE Transactions on Geoscience and Remote Sensing, 2003Many cases of remote sensing classification present complicated patterns that cannot be identified on the basis of spectral data alone, but require contextual methods that base class discrimination on the spatial relationships between the individual pixel and local and global configurations of neighboring pixels.
Binaghi E, Gallo I, Pepe M
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Supervised image classification by contextual AdaBoost based on posteriors in neighborhoods
IEEE Transactions on Geoscience and Remote Sensing, 2005AdaBoost, a machine learning technique, is employed for supervised classification of land-cover categories of geostatistical data. We introduce contextual classifiers based on neighboring pixels. First, posterior probabilities are calculated at all pixels.
Shinto Eguchi
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Histogram-Based Contextual Classification of SAR Images
IEEE Geoscience and Remote Sensing Letters, 2015We propose a spatially dependent mixture model for contextual classification of synthetic aperture radar (SAR) images. The proposed mixture model is based on the local image histograms modeled by multinomial densities. The contextual information is included into the mixture model both in the pixel and the class label domain by using local histograms ...
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Contextual classification in image analysis: an assessment of accuracy of ICM
Computational Statistics & Data Analysis, 1999zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Arbia, Giuseppe +2 more
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Contextual Image Classification Based on Spatial Boosting
2006 IEEE International Symposium on Geoscience and Remote Sensing, 2006Spatial AdaBoost proposed by Nishii and Eguchi (TGRS, 2005) is a supervised image classification method. It is a voting machine based on log posterior probabilities at a test pixel and its neighbors. The method can be obtained by less computation effort with respect to a classifier based on Markov random fields, but still shows a similar excellent ...
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A markov random field approach to spatio-temporal contextual image classification
IEEE Transactions on Geoscience and Remote Sensing, 2003Markov random fields (MRFs) provide a useful and theoretically well-established tool for integrating temporal contextual information into the classification process. In particular, when dealing with a sequence of temporal images, the usual MRF-based approach consists in adopting a "cascade" scheme, i.e., in propagating the temporal information from the
Sebastiano B Serpico
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Polarimetric SAR image classification based on contextual sparse representation
2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015A CSR-Based (Contextual Sparse Representation) classification method for PolSAR image is proposed based on the idea of sparse representation and spatial correlation, which incorporates the intrinsic polarimetric information and the spatial contextual information in the sparse representation procedure.
Lamei Zhang, Wooil M Moon
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