Abstract
Due to the application scenarios of image matching, different scenarios have different requirements for matching performance. Faced with this situation, people cannot accurately and timely find the information they need. Therefore, the research of image classification technology is very important. Image classification technology is one of the important research directions of computer vision and pattern recognition, but there are still few researches on volleyball image classification. The selected databases are the general database ImageNet library and COCO library. First, the color image is converted into a gray image through gray scale transformation, and then the scale space theory is integrated into the image feature point extraction process through the SIFT algorithm. Extract local feature points from the volleyball image, and then combine them with the Random Sample Consensus (RANSAC) algorithm to eliminate the resulting mismatch. Analyze the characteristic data to obtain the data that best reflects the image characteristics, and use the data to classify existing volleyball images. The algorithm can effectively reduce the amount of data and has high classification performance. It aims to improve the accuracy of image matching or reduce the time cost. This research has very important use value in practical applications.
Highlights
Image classification refers to the process of making judgments about image resolution. e primary key point is to export image features. e features of an image represent the basic or original features of the image
Image features are widely used in images, including texture classification [1, 2], moving object tracking [3], face recognition [4], and face detection [5]. e accuracy of feature point detection is directly related to the final image processing results, so the detection of image feature points has always been the focus of research. e same object in different images is matched by detecting local feature points of the image. e early feature point detection method is the video image matching algorithm based on corner detection proposed by Moravec [6]
Before using the similarity function for feature matching, Gaussian transformation is performed on the image, which makes the image feature points prominent and easy to distinguish; PEAnuta introduces the FFT cross-correlation algorithm, and the experimental research proves that it can improve matching efficiency; later, Fauqueur proposed a description algorithm for image shape, which can be summarized as using the histogram of the image edge direction. is method guarantees translation invariant features, and has a small amount of calculation
Summary
Image classification refers to the process of making judgments about image resolution. e primary key point is to export image features. e features of an image represent the basic or original features of the image. E same object in different images is matched by detecting local feature points of the image. E early feature point detection method is the video image matching algorithm based on corner detection proposed by Moravec [6]. Leutenegger et al [11] proposed the BRISK detection algorithm, which mainly uses the FAST algorithm for feature point detection on multiple scales of images. Ese methods first need to achieve image matching, for example, face recognition, for a person to collect images from different angles, through the detection of feature points, to achieve face recognition of the same person at different angles. Mikolajczyk et al [22, 23] compared the detection algorithms of various local regions of interest in images, mainly comparing the detection of feature points and image matching, and concluded that. SIFT features have great advantages in feature representation, matching, and recognition: SIFT features are local features of images, which are constant for scale changes, image rotation, brightness intensity, and strong resistance to viewing angle changes and noise. e SIFT algorithm is suitable for accurate and fast matching in a large amount of data, because a small number of objects can generate a large amount of SIFT feature vector information
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