Segmentation of multi-spectral images using the combined classifier approach

Pacĺık, Pavel; Duin, Rpw; Kempen, Geert M. P. van; Kohlus, Reinhard

doi:10.1016/s0262-8856(03)00013-1

Cited by 44 publications

(17 citation statements)

References 12 publications

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“…We have also recently experimented with several segmentation algorithms from the computer vision literature. Algorithms that are based on graph clustering [26], mode seeking [27] and classification [28] have been reported to be successful in moderately sized color images with relatively homogeneous structures. However, we could not apply these techniques successfully to our data sets because the huge amount of data in hyper-spectral images made processing infeasible due to both memory and computational requirements, and the detailed structure in high-resolution remotely sensed imagery prevented the use of sampling that has been often used to reduce the computational requirements of these techniques.…”

Section: Region Segmentationmentioning

confidence: 99%

Spatial Techniques for Image Classification

Aksoy¹

2006

Signal and Image Processing for Remote Sensing

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The constant increase in the amount and resolution of remotely sensed imagery necessitates development of intelligent systems for automatic processing and classification. We describe a Bayesian framework that uses spatial information for classification of high-resolution images. First, spectral and textural features are extracted for each pixel. Then, these features are quantized and are used to train Bayesian classifiers with discrete non-parametric density models. Next, an iterative split-and-merge algorithm is used to convert the pixel level classification maps into contiguous regions. Then, the resulting regions are modeled using the statistical summaries of their spectral, textural and shape properties, and are used with Bayesian classifiers to compute the final classification maps. Experiments with three ground truth data sets show the effectiveness of the proposed approach over traditional techniques that do not make strong use of region-based spatial information.

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Section: Region Segmentationmentioning

confidence: 99%

Spatial Techniques for Image Classification

Aksoy¹

2006

Signal and Image Processing for Remote Sensing

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“…For segmentation using color, we use the combined classifier approach in [6] because it fuses color and spatial information, and does not require the number of regions as an input parameter. First, an initial labeling of an image is done using k-means clustering of only the HSV values of pixels.…”

Section: Segmentation Using Color Informationmentioning

confidence: 99%

Semantic Scene Classification for Image Annotation and Retrieval

Çavuş

Aksoy

2008

Lecture Notes in Computer Science

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Abstract. We describe an annotation and retrieval framework that uses a semantic image representation by contextual modeling of images using occurrence probabilities of concepts and objects. First, images are segmented into regions using clustering of color features and line structures. Next, each image is modeled using the histogram of the types of its regions, and Bayesian classifiers are used to obtain the occurrence probabilities of concepts and objects using these histograms. Given the observation that a single class with the highest probability is not sufficient to model image content in an unconstrained data set with a large number of semantically overlapping classes, we use the concept/object probabilities as a new representation, and perform retrieval in the semantic space for further improvement of the categorization accuracy. Experiments on the TRECVID and Corel data sets show good performance.

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“…For example, segmentation algorithm, combined with scanning electronic microscopy and energydispersive x-ray microanalysis, has been used as spectral and spatial classifier. 124 Among such applications are the classifications of Cherenkov events. 125,126 Some numerical simulations have also been used in x-ray fluorescence holography to reconstruct atomic images.…”

Section: Applications Of Pattern Recognition Approaches To Xrsmentioning

confidence: 99%

Chemometrics and its applications to x‐ray spectrometry

Luo

2006

X-Ray Spectrometry

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Recent developments in curve fitting, multivariate calibration, and pattern recognition in chemometrics, and their application to x-ray spectrometry, are reviewed. Relatively innovated algorithms, namely genetic algorithms, neural networks and support vector machines, are discussed. Together with the three algorithms, the performances of different algorithms are compared briefly, which mainly includes principal component analysis, partial least-squares regression, factor analysis, cluster analysis, nearest neighbor methods, linear discriminant analysis, linear learning machine, and soft independent modeling of class analogy. In general, the chemometrics methods are superior to the conventional methods, such as Fourier transform and Marquardt-Levenberg algorithms, to a certain extent.

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Segmentation of multi-spectral images using the combined classifier approach

Cited by 44 publications

References 12 publications

Spatial Techniques for Image Classification

Spatial Techniques for Image Classification

Semantic Scene Classification for Image Annotation and Retrieval

Chemometrics and its applications to x‐ray spectrometry

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