Owing to their superior characteristics

local image descriptors have been widely used in many computer vision and image processing fields

such as image matching

image classification

image search

and structure from motion. This study proposes a new local feature called max edge orientation pattern (MEOP). First

the maximum intensity difference between the center pixel and surrounding ones is calculated. Second

the position and sign of the maximum intensity difference are encoded. The pixel with the maximum intensity difference denotes the strongest edge of the local adjacent region. The position of MEOP describes the radial direction

and the sign describes the arrow of the direction. Compared with the local binary pattern

the maximum edge direction pattern only encodes the maximum intensity difference. Therefore

the maximum edge direction pattern does not change as long as the position and sign of the maximum intensity difference do not change. The robustness of the maximum edge direction pattern is high

and its anti-noise ability is strong. The maximum edge direction pattern differs from the local binary pattern in describing the local structure of the image. Nevertheless

the two patterns are complementary in expressing the local structure of the image. Local rotation invariant coordinates are used to calculate the maximum edge orientation pattern. The rotation-invariant intensity-order space division method and multiple support regions are employed to pool the maximum edge orientation pattern and obtain a new local image descriptor

namely

maximum edge orientation pattern histogram (MEOPH). Compared with the MRRID descriptor using the local binary pattern

the MEOPH descriptor with the maximum edge direction pattern has different statistical properties and superior performance. With the standard test image set of the affine invariant research group of University of Oxford

image matching experiments are conducted on current popular descriptors

including SIFT

DAISY

CS-LBP

HRI-CSLTP

and MRRID. Experimental results on standard test image sets show that MEOPH and MRRID demonstrate the best performance. The matching performance of MEOPH is better than that of SIFT

DAISY

CS-LBP

and HRI-CSLTP in all test data sets and is slightly better than that of MRRID in most cases. The matching performance of MEOPH is much better than that of MRRID in the experiments wherein Gaussian noise is added to the standard test image sets. In addition

MEOPH and MRRID complement each other in image matching

and matching performance is significantly enhanced by the combination of the two descriptors. The superior performance of MEOPH in terms of stability makes the method suitable for local descriptor matching in complex environments. In the context of high-discrimination requirements in local descriptor matching

MEOPH can be used in conjunction with MRRID.