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DC poleHodnotaJazyk
dc.contributor.authorIonescu, Radu Tudor
dc.contributor.authorPopescu, Andreea Lavinia
dc.contributor.authorPopescu, Marius
dc.contributor.editorSkala, Václav
dc.date.accessioned2017-11-06T10:50:35Z
dc.date.available2017-11-06T10:50:35Z
dc.date.issued2014
dc.identifier.citationWSCG 2014: communication papers proceedings: 22nd International Conference in Central Europeon Computer Graphics, Visualization and Computer Visionin co-operation with EUROGRAPHICS Association, p. 111-118.en
dc.identifier.isbn978-80-86943-71-8
dc.identifier.uriwscg.zcu.cz/WSCG2014/!!_2014-WSCG-Communication.pdf
dc.identifier.urihttp://hdl.handle.net/11025/26405
dc.format8 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.relation.ispartofseriesWSCG 2014: communication papers proceedingsen
dc.rights@ Václav Skala - UNION Agencycs
dc.subjectjádrocs
dc.subjecthodnocení korelačního opatřenícs
dc.subjectordinální opatřenícs
dc.subjectvizuální slovacs
dc.subjecttextonycs
dc.subjecttexturní analýzacs
dc.subjecttexturní klasifikacecs
dc.titleTexture classification with the PQ kernelen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedComputer vision researchers have developed various learning methods based on the bag of words model for image related tasks, including image categorization, image retrieval and texture classification. In this model, images are represented as histograms of visual words (or textons) from a vocabulary that is obtained by clustering local image descriptors. Next, a classifier is trained on the data. Most often, the learning method is a kernel-based one. Various kernels can be plugged in to the kernel method. Popular choices, besides the linear kernel, are the intersection, the Hellinger’s, the c2 and the Jensen-Shannon kernels. Recent object recognition results indicate that the novel PQ kernel seems to improve the accuracy over most of the state of the art kernels. The PQ kernel is inspired from a set of rank correlation statistics specific for ordinal data, that are based on counting concordant and discordant pairs among two variables. In this paper, the PQ kernel is used for the first time for the task of texture classification. The PQ kernel is computed in O(nlogn) time using an efficient algorithm based on merge sort. The algorithm leverages the use of the PQ kernel for large vocabularies. Texture classification experiments are conducted to compare the PQ kernel with other state of the art kernels on two benchmark data sets of texture images. The PQ kernel has the best accuracy on both data sets. In terms of time, the PQ kernel becomes comparable with the state of the art Jensen-Shannon kernel. In conclusion, the PQ kernel can be used to obtain a better pairwise similarity between histograms, which, in turn, improves the texture classification accuracy.en
dc.subject.translatedkernel methodsen
dc.subject.translatedkernelen
dc.subject.translatedrank correlation measureen
dc.subject.translatedordinal measureen
dc.subject.translatedvisual wordsen
dc.subject.translatedtextonsen
dc.subject.translatedtexture analysisen
dc.subject.translatedtexture classificationen
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:WSCG 2014: Communication Papers Proceedings

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