Scale selective extended local binary pattern for texture classification

Abstract

In this paper, we propose a new texture descriptor, scale selective extended local binary pattern (SSELBP), to characterize texture images with scale variations. We first utilize multiscale extended local binary patterns (ELBP) with rotationinvariant and uniform mappings to capture robust local microand macro-features. Then, we build a scale space using Gaussian filters and calculate the histogram of multi-scale ELBPs for the image at each scale. Finally, we select the maximum values from the corresponding bins of multi-scale ELBP histograms at different scales as scale-invariant features. A comprehensive evaluation on public texture databases (KTH-TIPS and UMD) shows that the proposed SSELBP has high accuracy comparable to state-of-the-art texture descriptors on gray-scale-, rotation-, and scale-invariant texture classification but uses only one-third of the feature dimension.

Texture classification using block intensity and gradient difference (BIGD) descriptor

“…In contrast, as Table 3 shows, for the KTH-TIPS-2a database, Table 4. For consistency, we set parameter pair (L, s) for the Brodatz database as (15,3) and for the KTH-TIPS-2a database as (13,2), which correspond to the best classification performance in Tables 2 Table 4, we notice that the classification accuracy of two datasets keeps growing with the increase of K. Although K with a value larger than 128 may correspond to higher classification accuracy, for computational efficiency, we set K as 128 for all experiments unless mentioned otherwise.…”

“…Distinctive and robust representation of texture is the key for various multimedia applications such as image representation [1], texture retrieval [2], face recognition [3], image quality assessment [4,5], image/texture segmentation [6], dynamic texture/scene recognition [7,3], texture/color style transfer [8], and seismic interpretation [9]. Texture descriptors [10,11,12,13,14,15,16], which are robust against rotations and translations of images, are able to provide discriminative features.…”

A High-Speed, Real-Time Vision System for Texture Tracking and Thread Counting

“…It is worth noting that, for feature description in this paper, using typical texture features (e.g. [12], [13], [14], [15]) normally cannot satisfy both the robustness and the high-speed requirements simultaneously.…”

“…Taking Fig.3cas an example, we use the red column vector to represent the BRISK feature vector of the red MSER feature point. It is worth noting that, for feature description in this paper, using typical texture features (e.g [12],[13],[14],[15]…”

Texture retrieval using periodically extended and adaptive curvelets

“…Several authors [12][13][14][15][16][17][18][19][20][21][22] have developed texture-retrieval algorithms based on spatial domain content analysis. Ojala et al [12] developed local binary patterns (LBP) that decompose input images into sets of coefficients representing intensity differences between a reference pixel and its neighbors.…”