FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2908526535> ?p ?o ?g. }

• W2908526535 endingPage "30701" @default.
• W2908526535 startingPage "30693" @default.
• W2908526535 abstract "A novel method for texture image recognition is proposed in this paper. The aim of the proposed method is to represent texture by combining the Gabor wavelet transform and deep learning which are efficient techniques for image analysis. We developed the cumulative distribution function (cdf) space covariance model of Gabor wavelet (CSCM-GW), which can jointly model multivariate data in cdf space, in the Gabor wavelet domain to represent texture. The images having different sizes will be transformed by CSCM-GW into same size covariance matrices. Because CSCM-GW is based on the covariance matrix which belongs to Riemannian space, it has the high computational cost in the recognition phase. Therefore, we proposed a novel method of texture recognition called CSCM-GWF-CNN which uses CNN to project the fused covariance of CSCM-GW into low-dimensional vector space for reducing the computational cost and improving the recognition performance. The experiments on Brodatz (111) and KTH-TIPS2-b texture databases show that the proposed method is efficient for texture representation and outperforms most of the state-of-the-art recognition methods." @default.
• W2908526535 created "2019-01-25" @default.
• W2908526535 creator A5007621167 @default.
• W2908526535 creator A5015256062 @default.
• W2908526535 creator A5054690199 @default.
• W2908526535 creator A5064832662 @default.
• W2908526535 creator A5079355098 @default.
• W2908526535 creator A5091610964 @default.
• W2908526535 date "2019-01-01" @default.
• W2908526535 modified "2023-09-24" @default.
• W2908526535 title "CDF Space Covariance Matrix of Gabor Wavelet With Convolutional Neural Network for Texture Recognition" @default.
• W2908526535 cites W1485235332 @default.
• W2908526535 cites W1549083695 @default.
• W2908526535 cites W1909952827 @default.
• W2908526535 cites W1986869398 @default.
• W2908526535 cites W2005876975 @default.
• W2908526535 cites W2012222827 @default.
• W2908526535 cites W2033276808 @default.
• W2908526535 cites W2041101048 @default.
• W2908526535 cites W2048335335 @default.
• W2908526535 cites W2049893410 @default.
• W2908526535 cites W2053544201 @default.
• W2908526535 cites W2053996691 @default.
• W2908526535 cites W2059471177 @default.
• W2908526535 cites W2069820356 @default.
• W2908526535 cites W2072072671 @default.
• W2908526535 cites W2076063813 @default.
• W2908526535 cites W2082114499 @default.
• W2908526535 cites W2082855665 @default.
• W2908526535 cites W2098765040 @default.
• W2908526535 cites W2125837007 @default.
• W2908526535 cites W2128989367 @default.
• W2908526535 cites W2131081720 @default.
• W2908526535 cites W2131725398 @default.
• W2908526535 cites W2133848164 @default.
• W2908526535 cites W2135715315 @default.
• W2908526535 cites W2136218851 @default.
• W2908526535 cites W2139261503 @default.
• W2908526535 cites W2145947562 @default.
• W2908526535 cites W2147457207 @default.
• W2908526535 cites W2153786187 @default.
• W2908526535 cites W2154209944 @default.
• W2908526535 cites W2160547390 @default.
• W2908526535 cites W2163352848 @default.
• W2908526535 cites W2163534628 @default.
• W2908526535 cites W2293457052 @default.
• W2908526535 cites W2314785379 @default.
• W2908526535 cites W2343076010 @default.
• W2908526535 cites W2486038092 @default.
• W2908526535 cites W2518260411 @default.
• W2908526535 cites W2546677373 @default.
• W2908526535 cites W2600626704 @default.
• W2908526535 cites W2745398842 @default.
• W2908526535 cites W2748412105 @default.
• W2908526535 cites W2755766678 @default.
• W2908526535 cites W2765610253 @default.
• W2908526535 cites W2894957992 @default.
• W2908526535 cites W2962772276 @default.
• W2908526535 cites W2963239066 @default.
• W2908526535 cites W3102431071 @default.
• W2908526535 doi "https://doi.org/10.1109/access.2019.2891914" @default.
• W2908526535 hasPublicationYear "2019" @default.
• W2908526535 type Work @default.
• W2908526535 sameAs 2908526535 @default.
• W2908526535 citedByCount "4" @default.
• W2908526535 countsByYear W29085265352020 @default.
• W2908526535 countsByYear W29085265352021 @default.
• W2908526535 countsByYear W29085265352022 @default.
• W2908526535 crossrefType "journal-article" @default.
• W2908526535 hasAuthorship W2908526535A5007621167 @default.
• W2908526535 hasAuthorship W2908526535A5015256062 @default.
• W2908526535 hasAuthorship W2908526535A5054690199 @default.
• W2908526535 hasAuthorship W2908526535A5064832662 @default.
• W2908526535 hasAuthorship W2908526535A5079355098 @default.
• W2908526535 hasAuthorship W2908526535A5091610964 @default.
• W2908526535 hasBestOaLocation W29085265351 @default.
• W2908526535 hasConcept C11413529 @default.
• W2908526535 hasConcept C115961682 @default.
• W2908526535 hasConcept C136902061 @default.
• W2908526535 hasConcept C153180895 @default.
• W2908526535 hasConcept C154945302 @default.
• W2908526535 hasConcept C185142706 @default.
• W2908526535 hasConcept C196216189 @default.
• W2908526535 hasConcept C2781195486 @default.
• W2908526535 hasConcept C31972630 @default.
• W2908526535 hasConcept C41008148 @default.
• W2908526535 hasConcept C46286280 @default.
• W2908526535 hasConcept C47432892 @default.
• W2908526535 hasConcept C81363708 @default.
• W2908526535 hasConceptScore W2908526535C11413529 @default.
• W2908526535 hasConceptScore W2908526535C115961682 @default.
• W2908526535 hasConceptScore W2908526535C136902061 @default.
• W2908526535 hasConceptScore W2908526535C153180895 @default.
• W2908526535 hasConceptScore W2908526535C154945302 @default.
• W2908526535 hasConceptScore W2908526535C185142706 @default.
• W2908526535 hasConceptScore W2908526535C196216189 @default.
• W2908526535 hasConceptScore W2908526535C2781195486 @default.
• W2908526535 hasConceptScore W2908526535C31972630 @default.

• next