Noise-invariant structure pattern for image texture classification and retrieval

A local region in an image can be defined using centre pixel and its differences with neighboring pixels. In order to characterize different texture structure in a discriminating manner, this paper proposes twoframeworks of Noise Invariant Structure Pattern (NISP) which utilizes both the centre pixel and local and global information of an image. To replace the centre pixel, a threshold computed from adding centre pixel and intensity averages is used in the LBP code computation. For adding the magnitude information, binary patterns generated by taking thresholds involving centre pixel and local and global average contrast are adopted. Also for adding the information of individual neighborhood of a given pixel, the binary patterns generated from global thresholding of local averages are used. Based on the use of local and global information, this paper suggests two noise invariant models that are CNLP and CNGP (i.e. Completed Noise-invariant Local-structure Pattern and Global-structure Pattern). The proposed NISPs are also insensitive to noise as the centre pixel is not directly used as threshold. The proposed texture descriptors are tested on some of the representative texture databases like Outex, Curet, UIUC, Brodatz and XU - HR. The experimental results have shown that the proposed schemes can achieve higher classification and retrieval rates while being more robust to noise.