Classifying and modeling texture images, especially those with significant rotation, illumination, scale, and view-point variations, is a hot topic in the computer vision field. Inspired by local graph structure (LGS), local ternary patterns (LTP), and their variants, this paper proposes a novel image feature descriptor for texture and material classification, which we call Petersen Graph Multi-Orientation based Multi-Scale Ternary Pattern (PGMO-MSTP). PGMO-MSTP is a histogram representation that efficiently encodes the joint information within an image across feature and scale spaces, exploiting the concepts of both LTP-like and LGS-like descriptors, in order to overcome the shortcomings of these approaches. We first designed two single-scale horizontal and vertical Petersen Graph-based Ternary Pattern descriptors ($PGTP_{h}$and$PGTP_{v}$). The essence of$PGTP_{h}$and$PGTP_{v}$is to encode each$5\times 5$image patch, extending the ideas of the LTP and LGS concepts, according to relationships between pixels sampled in a variety of spatial arrangements (i.e., up, down, left, and right) of Petersen graph-shaped oriented sampling structures. The histograms obtained from the single-scale descriptors$PGTP_{h}$and$PGTP_{v}$are then combined, in order to build the effective multi-scale PGMO-MSTP model. Extensive experiments are conducted on sixteen challenging texture data sets, demonstrating that PGMO-MSTP can outperform state-of-the-art handcrafted texture descriptors and deep learning-based feature extraction approaches. Moreover, a statistical comparison based on the Wilcoxon signed rank test demonstrates that PGMO-MSTP performed the best over all tested data sets.

Writer identification is experiencing a revival of activity in recent years and continues to attract great deal of attention as a challenging and important area of research in the field of forensic and authentication. In this work, we introduce a reliable off-line system for text-independent writer identification of handwritten documents. Feature engineering is an essential component of a pattern recognition system, which can enhance or decrease the classification performance. A well-designed and defined feature extraction method improves the classification task. This paper proposes, for feature extraction, an effective, yet high-quality and conceptually simple feature image descriptor referred to as Cross multi-scale Locally encoded Gradient Patterns (CLGP). The proposed CLGP feature extraction method, which is expected to better represent salient local writing structure, operates at small observation regions (i.e., connected component sub-images) of the writing sample. CLGP histogram feature vectors computed from all these observation regions in all writing samples are considered as classification inputs to identify query writers using the Nearest Neighbor Classifier (1-NN). Our system is evaluated on six standard databases (IFN/ENIT, AHTID/MW, CVL, IAM, Firemaker, and ICDAR2011) including handwritten samples in Arabic, English, French, Greek, German, and Dutch languages. Comparing the identification performance with old and recent state-of-the-art methods, the proposed system achieves the highest performance on IFN/ENIT, AHTID/MW, and ICDAR2011 databases, and demonstrates competitive performance on IAM, CVL, and Firemaker databases.

This paper presents an efficient handcrafted texture operator for texture modeling and classification. The proposed descriptor, referred to as local directional decoded ternary pattern (LDDTP), consists in encoding both directional pattern features and contrast information in a compact way based on local derivative variations. The proposed operator first computes for each pixel within its 3×3 overlapping square neighborhood, on the one hand, central edge response through the 2ndderivative of Gaussian filter, and on the other hand, eight directional edge responses using the eight Frei-Chen masks to capture more detailed information. Then, spatial relationships among the neighboring pixels through the generated edge responses are exploited independently with the help of the concepts of LTP and LDP operators to enhance the discriminative power. Finally, the produced LDDTP pattern is splitted into two distinct parts: local directional decoded ternary pattern lower ( LDDTPL) and local directional decoded ternary pattern upper ( LDDTPU), which are combined into hybrid distributions to form the final LDDTP feature descriptor. Experimental results on eight publicly available texture datasets showed that the proposed LDDTP descriptor achieves classification performance, which is competitive or better than several old and recent state-of-the-art LBP variants.