目的 针对SIFT（scale invariant feature transform）特征描述方法存在特征维数高，计算效率低等问题，提出一种快速的，低维数的局部特征描述方法，即MN-CCH（mean normalized contrast context histogram）。方法 首先对局部特征区域内的像素进行均值规范化处理，得到局部特征区域的规范化对比度值。然后，在极坐标下以主方向为基准，将局部特征区域划分成32个子区域，统计每个子区域的正负对比度直方图。最后，对统计结果进行归一化消除线性光照的影响，得到64维的MN-CCH描述向量。结果 在图像变换数据集和小型图像检索数据库上的实验结果表明，64维的MN-CCH描述子可以达到与128维SIFT相当的匹配性能和相同的检索准确率，在描述子生成和匹配效率上明显优于SIFT方法，而且与同维数的CCH相比性能有明显的提高。结论 MN-CCH描述子在保留与SIFT相当性能的前提下，具有特征维数和计算效率的优势，更适合在一些对计算和存储资源要求较高的应用（如机器人导航、视觉SLAM等）中使用。

Objective Because the contrast value and the two-bin contrast histogram are used, contrast context histogram (CCH) has the characteristics of low dimension and efficient computation. The CCH contrast value is calculated between every pixel in a local region and the center pixel of the region making the CCH contrast values be sensitive to the intensity changing of the center pixel. Usually, the stability of the center intensity depends on the accuracy of the local feature detector position, which is susceptible to noise,image transformation and distortion, scale space discretization, and other factors. Furthermore contrast values in CCH are not constant illumination invariant because the intensity of the center point is not the mean intensity of the whole local region in most instances. As a result, CCH descriptors cannot get competitive performance when compared to the standard SIFT (scale invariant feature transform) descriptor. In addition, the SIFT descriptor has the characteristics of high dimension and low computational efficiency. In order to solve these problems, a fast and low-dimensional local descriptor is proposed, which is called mean normalized contrast context histogram (MN-CCH). Method MN-CCH firstly calculates the Gaussian weighted mean of the whole local region. The core of the Gaussian kernel coincides with the center of the local region. The local region is normalized by its weighted mean to obtain the region's normalized contrast values. Then, the local region is divided into 32 sub-regions in the log-polar coordinate system, and a two-bin positive-negative contrast histogram is built for each sub-region. To overcome the linear light changes, the 64-dimension MN-CCH vector is normalized to a unit vector. As the Gaussian weighted mean is used, MN-CCH contrast values are more stable for the feature location error when compared to the center point intensity used in CCH. In addition, the normalized contrast values are constant illumination invariant, which helps to improve the MN-CCH's performance. Result The image matching experiment in the Mikolajczyk image transformation dataset shows the proposed MN-CCH outperforms the CCH descriptor in most image transformations, especially in natural images and texture images. The image matching result also shows that the MN-CCH overcomes the low Recall in lower 1-Precision and noise-sensitive problems exist in the CCH descriptor. The proposed 64-dimension MN-CCH gets competitive performance when compared to 128-dimension SIFT descriptor in the image matching experiment. The retrieval test on the PCA-SIFT's retrieval dataset shows that MN-CCH gets the same retrieval accuracy as SIFT does, which is higher than the CCH descriptor. MN-CCH's descriptor building and matching time is indentical to the CCH as these two methods have the same dimension and complexity, which is more efficient than the SIFT descriptor as SIFT's tangential calculation in descriptor building is time-consuming and SIFT uses a higher descriptor dimension. Conclusion MN-CCH outperforms the CCH descriptor in both image matching and retrieval tests as it overcomes CCH's feature location error sensitive and contrast values are not constant illumination invariant, drawbacks. The proposed method has higher descriptor building speed and lower descriptor dimension, but it still gets competitive performance when compared to the SIFT descriptor. As a result, MN-CCH is more suitable in applications requiring high computing and large storages resources, such as mobile robot real-time navigation, visual SLAM and so on.