## 1 Introduction  
In recent years, the explosive development of 360-degree camera technology has led to rapid growth in 360-degree video, images, and multimedia viewing services, including social media and video-sharing websites such as Facebook and YouTube [1,2]. Users can easily take a 360-degree photo with a 360-degree camera and a smartphone. However, when a copyrighted artwork is either inadvertently or deliberately photographed without permission, copyright infringement occurs. Currently, manual inspections are performed of copyrighted contents in 360-degree multimedia. Thus, it is important to develop an automatic technology for identifying artwork for multimedia sharing systems before 360-degree multimedia are uploaded. To prevent unauthorized artwork from being illegally distributed by 360-degree multimedia viewing services, it is important to identify the artwork inside 360-degree images. Over the last decade, many computer vision algorithms have been proposed for extracting features, recognizing objects, and retrieving images [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65]. Although many algorithms have been used to extract features and identify objects, no technology specifically identifies artwork inside the 360-degree image.  
A 360-degree image is stored in an equirectangular projected format. The equirectangular projection maps the entire surface of a sphere onto a flat image [66, 67, 68, 69, 70]. The vertical axis is latitude and the horizontal axis is longitude. Because most locations in the equirectangular projected image are seriously distorted, the keypoints from the original artwork are either extremely difficult to match to those from the same artwork in the 360-degree image, or are matched to irrelevant objects. Therefore,we divide the 360-degree image into several portions and transform them into several rectilinear projected images. The rectilinear projection maps a portion of a sphere to a flat image. The feature extraction and matching procedures are performed based on the rectilinear projected images.  
The proposed method transforms the equirectangular projected 360-degree image to a sphere that has a radius of 1 and generates a polyhedron to wrap the sphere with polygons. Next, it locates several points on the sphere that are closest to the vertices of the polyhedron. The Euclidean distances between the points are used to determine the widths, heights, and directions of the rectilinear projections. With these parameters, the equirectangular projected 360-degree image is transformed into several rectilinear projected images. Identification is implemented by matching the features extracted from the transformed images to those in the original artwork. We also propose a method for improving the identification precision of artwork that is located in a highly distorted position by matching the keypoint's shapes. The shape of the keypoints is represented by the proportion of vector norms for the connected keypoints. By measuring the difference of the shapes of keypoints (DSK), we can identify false matches. Figure 1 provides a flowchart for the proposed method. In the experimental results section, we compare matching results before and after applying the proposed methods.