Locally Directional and Extremal Pattern for Texture Classification

Abstract

An image texture was defined in terms of pixel intensities and directionality. However, most of the current texture representation methods did not consider the two key factors simultaneously. To effectively capture the directional and pixel intensity information of texture, in this paper, we propose a novel and robust local descriptor, named locally directional and extremal pattern (LDEP), for texture classification. It extracts directional local difference count pattern (DLDCP) being made up of DLDCP in the odd positions and DLDCP in the even positions to express directional information in the local area in the first place. Furthermore, to acquire the extremum information remained by DLDCP, by concatenating extremum location pattern (ELP), extremum difference pattern (EDP), and extremum compression pattern (ECP) from the sampling points, we extract a neighbors extremum related local pattern (NERLP). The experimental results obtained from four representative texture databases (Prague, Stex, UIUC, Kth-tips2-a, Brodatz, and CUReT) demonstrate that our proposed LDEP descriptor can achieve comparable accurate classification rates in different conditions (rotation, illumination, scale variation, viewpoint variation, and noise) with ten typical texture classification methods. INDEX TERMS Directional pattern, extremal pattern, local pattern, texture representation, texture classification.

A Texture-Hidden Anti-Counterfeiting QR Code and Authentication Method

“…The identification effects of the corner methods (SIFT [ 34 ], SURF [ 35 ], and BRISK [ 36 ]) are relatively general. Some representative texture algorithms (2LQR [ 24 ], LBP [ 37 ], CLBP [ 38 ], CLBC [ 39 ] ECLBP [ 40 ], COV_LBPD [ 41 ], MRELBP [ 42 ], JRLP [ 43 ], MCDR [ 44 ], RALBGC [ 45 ], LDEP [ 46 ], and LGONBP [ 47 ]) can also achieve good results with accuracies above 99%, and some algorithms are close to or reach 100%. In terms of time, to process an image of the same size, the proposed algorithm DFDA needs 0.25 s, which is at the upper level.…”

“…In terms of time, to process an image of the same size, the proposed algorithm DFDA needs 0.25 s, which is at the upper level. The shortest time is obtained by [ 24 ], only 0.01 s and the longest time is from [ 46 ], 146.3 s, followed by [ 34 ], 17.08 s. The rest of the methods are within 2 s.…”

“…Table 11 shows the accuracy results of the different methods for different reduced proportions. The accuracy of [ 34 , 35 , 36 , 37 , 38 , 39 , 46 , 47 ] and other methods is above 90% when the size is 12%, 24%, or 36% of the original figure. The accuracy of [ 34 , 38 , 39 , 40 , 46 , 47 ] and other methods is more than 90% when the size of the original image is 64%.…”

Dynamic Facial Expression Understanding Using Deep Spatiotemporal LDSP On Spark

“…Later, Khadiri et al [31] proposed the local directional ternary pattern (LDTP) for texture understanding, where edge responses are extracted by employing eight Frei-Chen masks. For texture analysis, a locally directional and extremal pattern (LDEP)-based handcrafted descriptor was introduced in [32]. The LDEP mainly comprises directional local difference count patterns and neighboring extremum-related features.…”

A New Feature-Based Wavelet Completed Local Ternary Pattern (Feat-WCLTP) for Texture Image Classification

“…Texture classification is increasingly recognised as a serious issue in the texture analysis field [1]. As it plays a key role in a wide variety of real-life applications such as medical images analysis [2]- [4], human detector [5], human action recognition [6], manufacturing industry [7], image segmentation [8], remote sensing [9], object tracking [10], [11], face recognition [12], [13], and image retrieval [14], [15].…”