Search

International audience; In this paper, we propose a micro-macro feature combination approach for texture classification. The two disparate yet complementary categories of features are combined. By this way, Local Binary Pattern (LBP) plays the role of micro-structure feature extractor while the scattering transform captures macro-structure information. In fact, for extracting the macro-type features, coefficients are aggregated from three different layers of the scattering network. It is a handcrafted convolution network which is implemented by computing consecutively wavelet transforms and modulus non-linear operators. By contrast, in order to extract micro-structure features which are rotation-invariant, relatively robust to noise and illumination change, the completed LBP is utilized alongside the biologically-inspired filtering (BF) preprocessing technique. Overall, since the proposed framework can exploit the advantages of both feature types, its texture representation is not only invariant to rotation, scaling, illumination change but also highly discriminative. Intensive experiments conducted on many texture benchmarks such as CUReT, UIUC, KTH-TIPS-2b, and OUTEX show that our framework has a competitive classification accuracy.

International audience; 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.

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.