Feature Description Using Center-Symmetric Extended Local Ternary Patterns
Wen‐Hung Liao, Chia‐Yu Liu, Ming-Ching Lin · 2014
Effective recognition of objects calls for the appropriate selection of feature descriptor. In this paper, we generalize the "extended local ternary patterns" (ELTP) to form a novel and compact set of features named center-symmetric extended local ternary patterns (CS-ELTP). The newly defined CS-ELTP follows a simplified encoding procedure and has a lower dimension for a fixed neighborhood region. It achieves good balances among feature dimension, recognition rate and noise resistance according to our comparative experimental analysis. In addition, we combine binary and ternary patterns to create a class of hybrid descriptor that possesses the characteristics of both types of descriptor. Experimental results indicate that the hybrid descriptor can improve the performance in noisy conditions while maintaining a reasonable feature dimension.