Texture Description with Completed Local Quantized Patterns

Local binary patterns (LBP) has been very successful in a number of areas, including texture analysis and face analysis. Recently, local quantized patterns (LQP) was proposed to use vector quantization to code complicated patterns with a large number of neighbors and several quantization levels. It uses lookup table technique to map patterns into the corresponding indices. In this paper, we propose completed local quantized patterns (CLQP) for improving the performance of LQP. Firstly, we find that LQP only considers the sign-based difference, it thus misses some discriminative information. We therefore propose to use the magnitude-based and orientation-based differences to complement the sign-based difference for LQP. We finally use vector quantization to learn three separate codebooks for local sign, magnitude and orientation patterns, respectively. Secondly, we also observe that LQP uses random initialization in vector quantization, this leads to losing the distribution of local patterns and costing much computational time. For reducing the unnecessary computational time of initialization, we use preselected dominant patterns as the initialization. Our experimental results show that CLQP outperforms well-established features including LBP, LTP, CLBP, LQP on a range of challenging texture classification problems and an infant pain detection problem.