Self-organizing maps for texture classification

Petrov, Nedyalko; Georgieva, Antoniya; Jordanov, Ivan

doi:10.1007/s00521-011-0797-x

Cited by 13 publications

(4 citation statements)

References 18 publications

(32 reference statements)

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“…At present, SOM was widely used in the land research field, because it can better identify the complex and nonlinear relationship between the components of cultivated land productivity. From the early reports, it could be found that SOMs were applied to land management [34], soil classification [35][36][37][38], and cultivated land productivity zoning [39,40].…”

Section: Self-organizing Feature Mapmentioning

confidence: 99%

Study on Land Consolidation Zoning in Hubei Province Based on the Coupling of Neural Network and Cluster Analysis

Xiao

Zhao

Yong

et al. 2021

Land

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Land consolidation zoning is the basis of land reclamation planning and provides a precondition for land management work. Zoning is beneficial to scientific decision-making regarding the use of cultivated land resources, and helps to ensure the quality and production level of cultivated land. Hence, land consolidation zoning is of national significance in terms of safeguarding food security. In this paper, an indicator system for land evaluation and zoning is described based on indicators of natural ecological conditions, socioeconomic conditions, land-use conditions, and the land consolidation potential of Hubei province. The application of self-organizing maps and Hierarchical clustering results in 11 and 12 categories, respectively. After comparing and adjusting these results according to the real situation and key points in the land consolidation of each zone, seven integrated categories are determined. Finally, the land consolidation zoning scheme for these seven integrated partitions is shown to be suitable for Hubei province and in line with the real situation. The proposed scheme offers support for land remediation in a coordinated and orderly way, and provides a scientific basis for improving land utilization efficiency and grain production capacity in Hubei province.

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Section: Self-organizing Feature Mapmentioning

confidence: 99%

Study on Land Consolidation Zoning in Hubei Province Based on the Coupling of Neural Network and Cluster Analysis

Xiao

Zhao

Yong

et al. 2021

Land

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“…In general, there methods search for a lower dimensional space that can best represent the data. Some of the most broadly used approaches include principal component analysis (PCA), linear discriminant analysis (LDA) and their modifications [19,20,21]. However they will be a subject for a further extension of the current work.…”

Section: Data Reductionmentioning

confidence: 99%

Radar Emitter Signals Recognition and Classification with Feedforward Networks

Petrov

Jordanov

Roe³

2013

Procedia Computer Science

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A possible application of neural networks for timely and reliable recognition of radar signal emitters is investigated. In particular, a large data set of intercepted generic radar signal samples is used for investigating and evaluating several neural network topologies, training parameters, input and output coding and machine learning facilitating data transformations. Three case studies are discussed, where in the first two the radar signals are classified in two broad classes -with civil or military application, based on patterns in their pulse train characteristics and in the third one trained to distinguish between several more specific radar functions. Very competitive results of about 82%, 84% and 67% are achieved on the testing data sets.

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“…In traditional machine learning problems, we usually assume that a data point has a feature vector to represent its input information. For example, in image recognition problem, we can extract a visual feature vector from an image, using a texture descriptor [36,34,33,52,32,15,49,20]. In this scene, the texture is a view of the image.…”

Section: Introductionmentioning

confidence: 99%

Supervised multiview learning based on simultaneous learning of multiview intact and single view classifier

Wang

Yue

et al. 2016

Neural Comput & Applic

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Multiview learning problem refers to the problem of learning a classifier from multiple view data. In this data set, each data points is presented by multiple different views. In this paper, we propose a novel method for this problem. This method is based on two assumptions. The first assumption is that each data point has an intact feature vector, and each view is obtained by a linear transformation from the intact vector. The second assumption is that the intact vectors are discriminative, and in the intact space, we have a linear classifier to separate the positive class from the negative class. We define an intact vector for each data point, and a view-conditional transformation matrix for each view, and propose to reconstruct the multiple view feature vectors by the product of the corresponding intact vectors and transformation matrices. Moreover, we also propose a linear classifier in the intact space, and learn it jointly with the intact vectors. The learning problem is modeled by a minimization problem, and the objective function is composed of a Cauchy error estimator-based view-conditional reconstruction term over all data points and views, and a classification error term measured by hinge loss over all the intact vectors of all the data points. Some regularization terms are also imposed to different variables in the objective function. The minimization problem is solve by an iterative algorithm using alternate optimization strategy and gradient descent algorithm. The proposed algorithm shows it advantage in the compression to other multiview learning algorithms on benchmark data sets.

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Self-organizing maps for texture classification

Cited by 13 publications

References 18 publications

Study on Land Consolidation Zoning in Hubei Province Based on the Coupling of Neural Network and Cluster Analysis

Study on Land Consolidation Zoning in Hubei Province Based on the Coupling of Neural Network and Cluster Analysis

Radar Emitter Signals Recognition and Classification with Feedforward Networks

Supervised multiview learning based on simultaneous learning of multiview intact and single view classifier

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