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Image matching based on LBP and SIFT descriptor

In this paper, we propose a new approach for extracting invariant feature from interest region. The new descriptor is inspired from the original descriptor SIFT (Scale Invariant Feature Transform) which is widely used in image matching by extracting interest points (IPs). However, this descriptor performs badly when the background is complex or corrupted with noise. Then, we adopt the local binary Pattern (LBP) descriptor with uniform pattern and the center symmetric local binary pattern (CSLBP) instead of a gradient feature used in the SIFT algorithm. To do so, we present new descriptors based on different combinations of SIFT, LBP and CSLBP descriptors to improve matching results. Thus, we compute different evaluation measures such as repeatability, recall and precision for various images transformations (blur attack, rotation and affine transformation). Experiments, which are achieved on two different databases, show that the descriptors leads to better results.