A scattering transform combination with local binary pattern for texture classification
| العنوان: | A scattering transform combination with local binary pattern for texture classification |
|---|---|
| المؤلفون: | Ngoc-Son Vu, Vu-Lam Nguyen, Philippe-Henri Gosselin |
| المساهمون: | Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY) |
| المصدر: | CBMI 2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI) International Workshop on Content-based Multimedia Indexing International Workshop on Content-based Multimedia Indexing, Jun 2016, Bucharest, Romania. ⟨10.1109/CBMI.2016.7500238⟩ |
| بيانات النشر: | IEEE, 2016. |
| سنة النشر: | 2016 |
| مصطلحات موضوعية: | Contextual image classification, Image classification, Computer science, business.industry, Local binary patterns, Feature extraction, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020207 software engineering, Pattern recognition, 02 engineering and technology, Texture (music), Image texture analysis, Image texture, Feature (computer vision), Principal component analysis, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, business, Feature detection (computer vision) |
| الوصف: | International audience; In this paper, we propose a combined feature approach which takes full advantages of local structure information and the more global one for improving texture image classification results. In this way, Local Binary Pattern is used for extracting local features, whilst the Scattering Transform feature plays the role of a global descriptor. Intensive experiments conducted on many texture benchmarks such as ALOT, CUReT, KTH-TIPS2-a, KTH-TIPS2b, and OUTEX show that the combined method outweigh each one which stands alone in term of classification accuracy. Also, our method outperforms many others, whilst it is comparable to state of the art on the experimented datasets. |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::12017b45a6940d21b0813ee1d92f5c4fTest https://doi.org/10.1109/cbmi.2016.7500238Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....12017b45a6940d21b0813ee1d92f5c4f |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |