Image processing has been widely used in various fields due to the rapid development of computer vision. Accurate and robust image feature extraction is the premise of image analysis and recognition in image processing. Many state-of-the-art feature description algorithms are available

and local intensity order pattern (LIOP) is one of them. However

in existing feature description algorithms based on LIOP

the information between sampling points is not fully considered during weight calculation

and redundant intensity order patterns exist during the construction of the feature descriptor. These conditions result in an inaccurate feature descriptor. To solve this problem

an improved algorithm is proposed in this study. The algorithm maximizes the use of information between sampling points and eliminates redundant patterns. The LIOP algorithm shows that a feature descriptor is obtained by adding the weight of the index value of the corresponding order pattern. Therefore

the weight of each pattern affects the precision and accuracy of the feature descriptor

and the information on the sampling point is crucial. First

investigation of the local information of the sampling point indicates that the information on the local patch is not fully utilized

and redundant patterns exist. Therefore

an algorithm that calculates the weight of the feature descriptor is constructed by combining the order structure information of the sampling points. Second

with the increase in the sampling points

the patterns increase rapidly

and the dimension of the feature descriptor expands rapidly. These occurrences result in the curse of dimensionality. The relationship between the intensity order pattern and its corresponding weight is then analyzed. The contribution of the low-weight patterns is small when structuring the feature descriptor because these patterns are decided by the value of the sampling pixel points. High-weight patterns contribute significantly to the feature descriptor. Noise also causes the feature descriptor to become inaccurate. Analysis reveals the presence of redundant patterns

which need to be removed to make the feature descriptor reduction robust. Therefore

the redundant patterns are eliminated when constructing the feature descriptor. The obtained feature descriptor makes full use of local information and eliminates redundant patterns. The proposed algorithm is used to experiment on a standard dataset (Oxford Dataset) and four other complex illumination images. A total of 132 dimensional feature descriptors are obtained. Compared with the original algorithm of LIOP

the proposed algorithm can effectively improve the precision-recall curve and description ability without increasing the feature dimensions. In addition

the robustness of the feature descriptor is enhanced in terms of monotonic intensity and rotation change. Therefore

the improved feature descriptor demonstrates better performance than the original feature descriptor. The proposed algorithm for constructing a feature descriptor makes full use of the difference and structural information between sampling points and eliminates redundant patterns. The performance and robustness of the feature descriptor are improved

and its dimension is reduced. The algorithm obtains high identification accuracy even under complex illumination.