## 2 Research Background  
A 360-degree image is achieved by stitching several individual images together based on the feature matching method [66; 67]. The images are photographs taken from different angles. The first step is to extract the local features. One frequently used feature is the scale invariant feature transform (SIFT). To establish relationships between the individual images, the features are matched to each other. An estimation procedure, the random sample consensus (RANSAC) algorithm, is used to remove outliers and retain inliers that are compatible with a homography between the individual images. Next, a set of correct matches is selected by verifying the matches that were based on the inliers. A bundle adjustment procedure further refines the estimated distortion factors. To ensure smooth transitions between images, a blending procedure is applied to the overlapped images. Finally, the 360-degree image is produced by projecting the stitched image onto spherical formats for viewing. The most commonly used format is the equirectangular projection. Most of the regions in the equirectangular projected image are seriously distorted. The photograph of the artwork is affected by several image processing operations during the stitching and projection. Therefore, the keypoints from the original artwork either are extremely difficult to match to those from the same artwork in the 360-degree image, or are matched to irrelevant objects. Figure 1: Flowchart of the proposed method. In the last decade, several robust algorithms have been proposed for extracting features from images in a variety of computer vision applications. One of the most famous of these algorithms is the SIFT, which detects interest points, specifically keypoints, and assigns orientation properties to each keypoint based on the direction of the local gradient. In the field of facial recognition, the SIFT can detect specific facial features and performs well [11; 12; 13; 14; 15]. In [14], the SIFT was used to extract 3D geometry information from 3D face shapes to recognize 3D facial expressions. The SIFT was also used to recognize specific objects, such as TV logos [16] and regular art pictures [17]. However, the art pictures recognized were merely ordinarily photographed pictures. Content-based image retrieval (CBIR) is an application that searches images that are similar to the query image in a database. Because of the SIFT's rotation, scale and translation invariant properties, it demonstrated adequate retrieval performance in the CBIR evaluation [18; 19]. However, because of the SIFT's high computation complexity, researchers have proposed additional methods to increase its speed. The speeded-up robust features (SURF) were partially inspired by the SIFT. The descriptor is composed of a histogram constructed from several gradient orientations that surround a keypoint. It is obtained based on integral images for convolutions and the strength of both a Hessian matrix- and a distribution-based descriptor. The length of the standard SIFT descriptor is 128, whereas that of the SURF descriptor is condensed to 64. In [21], the authors used SURF descriptors for image retrieval and classification. They extracted interest points from images in combination with the bag-of-words method to obtain adequate retrieval and classification results. In [22; 23], the authors used SURF detectors and descriptors for facial recognition. The keypoints were extracted from facial images following image processing procedures, such as histogram equalization and normalization. The gradients for the neighborhoods of keypoints were used to describe person-specific features. The features from an accelerated segment test (FAST) were proposed to reduce the computational complexity for real-time application. The FAST detector performs tests by examining a circle of 16 pixels to detect features. When three of the 16 pixels are greater or less than the intensity of the center pixel, a feature can be detected.