Support vector domain description

Tax, David M. J.; Duin, Robert P. W.

doi:10.1016/s0167-8655(99)00087-2

Cited by 1,378 publications

(806 citation statements)

References 3 publications

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“…Classification is used to tailor the data, so that the given segmentation algorithm performs better. One-class classifiers or novelty detectors having strong discriminative power such as a one-class Support Vector Machine (SVM) [56]/support vector domain descriptor [57] and others [58], may be employed to generate a preliminary classification or probability image (with additional processes, described below) of pixel membership based on the spectral content encapsulated within provided reference segments. Such an initial classification may provide useful information in describing thematic and spectral similarities that may assist in segmentation.…”

Section: Exploiting Spectral Data Contained Within Provided Referencementioning

confidence: 99%

Classifier Directed Data Hybridization for Geographic Sample Supervised Segment Generation

Fourie

Schöepfer

2014

Remote Sensing

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Quality segment generation is a well-known challenge and research objective within Geographic Object-based Image Analysis (GEOBIA). Although methodological avenues within GEOBIA are diverse, segmentation commonly plays a central role in most approaches, influencing and being influenced by surrounding processes. A general approach using supervised quality measures, specifically user provided reference segments, suggest casting the parameters of a given segmentation algorithm as a multidimensional search problem. In such a sample supervised segment generation approach, spatial metrics observing the user provided reference segments may drive the search process. The search is commonly performed by metaheuristics. A novel sample supervised segment generation approach is presented in this work, where the spectral content of provided reference segments is queried. A one-class classification process using spectral information from inside the provided reference segments is used to generate a probability image, which in turn is employed to direct a hybridization of the original input imagery. Segmentation is performed on such a hybrid image. These processes are adjustable, interdependent and form a part of the search problem. Results are presented detailing the performances of four method variants compared to the generic sample supervised segment generation approach, under various conditions in terms of resultant segment quality, required computing time and search process characteristics. Multiple metrics, metaheuristics and segmentation algorithms are tested with this approach. Using the spectral data contained within user OPEN ACCESS Remote Sens. 2014, 6 11853 provided reference segments to tailor the output generally improves the results in the investigated problem contexts, but at the expense of additional required computing time.

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Section: Exploiting Spectral Data Contained Within Provided Referencementioning

confidence: 99%

Classifier Directed Data Hybridization for Geographic Sample Supervised Segment Generation

Fourie

Schöepfer

2014

Remote Sensing

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“…The score for new points is produced by computing the distance to the sphere's center. SVDD [30,39,31] follows this approach by finding the minimal enclosing sphere which surrounds the data. Formulated as a quadratic program, it is further possible to allow for noisy measurements by discarding a certain fraction of training points.…”

Section: Support Vector Data Descriptionmentioning

confidence: 99%

“…This allows building flexible models for data with large intra-class variation, such as Raman spectra. Our method is highly related to the Support Vector Data Description [30,31] which proved to be applicable for various one-class classification problems [32,33,34].…”

Section: Introductionmentioning

confidence: 99%

Automatic identification of novel bacteria using Raman spectroscopy and Gaussian processes

Kemmler

Rodner

Rösch

et al. 2013

Analytica Chimica Acta

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Raman spectroscopy is successfully used for the reliable classification of complex biological samples. Much effort concentrates on the accurate prediction of known categories for highly relevant tasks in a wide area of applications such as cancer detection and bacteria recognition. However, the resulting recognition systems cannot always be directly used in practice since unseen samples might not belong to classes present in the training set. Our work aims to tackle this problem of novelty detection using a recently proposed approach based on Gaussian processes. By learning novelty scores for a large bacteria Raman dataset comprising 50 different strains, we analyze the behavior of this method on an independent dataset which includes known as well as unknown categories. Our experiment reveals that Gaussian processes can be successfully applied to the task of finding unknown bacterial strains, leading to superior results compared to state-of-the-art methods such as Gaussian mixture models and Support Vector Data Description.

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“…Several different terms have been used to refer to oneclass classification, such as outlier detection, novelty detection, and concept learning [7,25]. These various terms are usually used to represent the various problems with one-class classification.…”

Section: One-class Classificationmentioning

confidence: 99%