A novel wood feature extraction method based on improved blocked higher-order local auto-correlation

Traditionally, HLAC (Higher-order Local Auto-Correlation) algorithm was used to extract texture features of wood images. However, heavy memory consumption and complexity of high-order mask pattern were common in HLAC. A novel feature extraction strategy based on improved blocked higher-order local auto-correlation (IBHLAC) is proposed to circumvent these problems. Initially, sequences of the whole wood image frames, which are the grayscale treatment, were being divided into series of subdivisions vertically and horizontally. Additionally, to enhance auto-correlation ability of the proposed method, different high-order patterns of masks were rebuilt based on zero-order mask by introducing the morphology and affine transformation. Finally, time-consumption and memory occupation of related four methods were compared. Experiment results indicated IBHLAC costs less time and fewer memory consumption on the wood texture database compared with other methods, which reveal that IBHLAC is efficient.