An integrated descriptor for texture classification

Regarding texture features, Local-based methods such as Local Binary Pattern (LBP) and its variants are computationally efficient high-performing but sensitive to noise, and suffering global structure information loss. By contrast, filter-based counterparts, the Scattering Transform for instance, are tolerant to noise and translation but often lack of small local structure information. In this paper we propose an integration of those to take full advantages of both local and global features. In this way, LBP is used for extracting local features while the Scattering Transform feature plays the role of a global descriptor. In addition to the combination of these two state-of-the-art features, we further integrate a new preprocessing technique called biologically-inspired filtering (BF) as well as an efficient PCA classifier. Intensive experiments conducted on many texture benchmarks such as CUReT, UIUC, KTH-TIPS2b, and OUTEX show that our combined method not only outweighs each one which stands alone but also competes with state-of-the-art on the experimented datasets.