# Heterogeneous Image Matching via a Novel Feature Describing Model
## 1 Introduction  
Computer vision techniques have been widely applied in many areas such as object tracking, medical image analysis, and pattern recognition. Image matching can help to find the similar content in different images by analyzing the pixel values and their potential features [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12]. Traditional image matching techniques are developed based on different principles and information using skills. Some of them directly use the original pixel values in a certain region, and it is easy to operate and work well in some simple scenes [13; 14; 15; 16; 17]. It often requires more computation costs and is sensitive to the geometrical differences. Some others process the matching computing based on features extraction instead of directly on the pixel values [2; 3; 6; 8; 14; 18]. Invariance can be achieved or partly achieved in some procedures such as rotation, resizing, translation, and so on. After the features are detected, matching computing will be processed on a certain similarity. Some local feature detection methods have been presented in the past years, and it was put forward firstly by [PERSON] [13] and [PERSON] [1]. [PERSON] and [PERSON] introduced auto-correlation function and auto-correlation matrix independently to find feature points (simple corners). However, for the extracted feature points, the former is not adaptive to rotation and noise, the latter is adaptive to rotation and illumination, does not have scale invariance. [PERSON] and [PERSON] the Harris, and put forward the Shi-Tomasi [19], which makes the distribution of extracted feature points more uniform and reasonable. It is computationally expensive and the feature points are not scale invariant. SUSAN (smallest univalue segment assimilating nucleus) is proposed to determine corners by the number of pixels in the preset region. The algorithm is small in computation and does not have scale invariance [4]. Then, a machine learning method is used to compute FAST (features from accelerated segment test) [20]. It can calculate faster, but when there are noise in the image, there will be more error feature points. Based on FAST, [PERSON] put forward AGAST (adaptive and generic corner detection based on the accelerated segment test), which effectively improves the speed of feature point extraction [21]. However, none of them have scale invariance. KAZE descriptor used a nonlinear diffusion filter to construct a stable nonlinear scale space. It takes a long time to construct the nonlinear scale space, and more octaves can reduce the efficiency of KAZE [22]. With the rapid development of machine learning, some new methods, including sparse coding and convolutional neural network (CNN), are applied to feature extraction [23; 24; 25; 26; 27; 28; 29; 30]. Sparse coding is a linear combination that reconstructs the input data into a set of hypercomplete basis vectors. The new features of input data are composed of the coefficients of base vectors. Sparse coding is very slow in practice. The eigenvalue is obtained by a convex optimization method in the test phase. From the structure perspective, the network can use the increased nonlinearity to get the approximate structure of the objective function, and at the same time get a better representation of the characteristics by increasing the depth of CNN. However, it also increases the overall complexity of the network, making it difficult to optimize the network and easy to over fit. Scale-Invariant Feature Transform (SIFT) is proposed to improve the features invariance of previous methods [3; 6]. Image pyramid and local gradient information statistics are introduced to compute the 128-dimensional features. It can work well even if there exist some changes in scale, brightness, viewing angle, etc. However, the computation and the matching error rate are still considerable. Some techniques based on SIFT have been developed over the past years.