Pixel to Patch Sampling Structure and Local Neighboring Intensity Relationship Patterns for Texture Classification

Kai Wang Charles-Edmond Bichot 1 Chao Zhu 1 Bailin Li
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : In this paper we explore local image descriptors for texture classification. We mainly propose two novel contributions: an effective sampling structure based on Pixel To Patch (PTP) to mimic the retinal sampling pattern; and a novel Local Neighboring Intensity Relationship Pattern (LNIRP) descriptor to extract texture feature by exploring neighboring gray-scale properties. The LNIRP descriptor is extended by using the PTP sampling structure which aims to capture not only micro-patterns but also macro-patterns, while reducing feature dimensionality and improving computational efficiency. The proposed descriptor has advantages of robustness to image rotation, computational simplicity, no texton dictionary learning step and training-free. Moreover, the LNIRP descriptor is complementary to the Local Binary Pattern (LBP) descriptor. Extensive experiments were conducted on Outex database to evaluate the proposed descriptor and sampling structure. The proposed descriptor can achieve superior classification performance compared to most of the state-of-the-art methods, including what we believe to be the best results reported for Outex, while offering a smallest feature dimension.
Type de document :
Article dans une revue
IEEE Signal Processing Letters, 2013, 9, 20, pp.853-856. 〈10.1109/LSP.2013.2270405 〉
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01339227
Contributeur : Équipe Gestionnaire Des Publications Si Liris <>
Soumis le : mercredi 29 juin 2016 - 15:49:33
Dernière modification le : vendredi 10 novembre 2017 - 01:18:22

Identifiants

Collections

Citation

Kai Wang, Charles-Edmond Bichot, Chao Zhu, Bailin Li. Pixel to Patch Sampling Structure and Local Neighboring Intensity Relationship Patterns for Texture Classification. IEEE Signal Processing Letters, 2013, 9, 20, pp.853-856. 〈10.1109/LSP.2013.2270405 〉. 〈hal-01339227〉

Partager

Métriques

Consultations de la notice

70