P-SURF: A Robust Local Image Descriptor

SIFT-like representations are considered as being most resistant to common deformations, although their computational burden is heavy for low-computation applications such as mobile image retrieval. H. Bay et. al. proposed an efficient implementation of SIFT called SURF. Although this descriptor has been able to represent the nature of some underlying image patterns, it is not enough to represent more complicated ones. Also, the proposed high-dimensional alternative to SURF indeed improves the distinctive character of the descriptor, while it appears to be less robust. In this paper, an enhanced version of SURF is proposed. Specifically, it consists of two components: the feature representation for independent intensity changes and the coupling description for these intensity changes. To this end, phase space is introduced to model the relationships between the intensity changes and several statistic metrics quantizing these relationships are also proposed to meet practical demands. The feature matching experiments demonstrate that our method achieves a favorable performance close to that of SIFT and faster construction-speed. We also present results showing that the use of the enhanced SURF representation in a mobile image retrieval application results in a comparable performance to SIFT.