# Image Retrieval Method Based on Image Feature Fusion and Discrete Cosine Transform
## 1 Introduction  
As the amount of available data grows, the explosion of images has brought significant challenges to automated image search and retrieval. While the explosive growth of internet data provides richer information and more choices, it also means that users need to browse an ever-increasing amount of content to retrieve the desired results. Therefore, effectively integrating multiple-media information on the internet to assist users in quick and efficient retrieval has become a new hot issue in image retrieval. Traditional image retrieval technology is mainly divided into text-based image retrieval and content-based image retrieval [1]. Text-based image retrieval [2] first requires manual annotation or machine learning to generate corresponding text annotations for the images in a database, then calculates the distance between the annotations and query words and sorts them in ascending order to produce a search list. Text-based retrieval is still the primary retrieval method used by various retrieval machines today. Its main advantage is that it can retrieve related images quickly. Still, its disadvantage is that the text information contains more irrelevant information. It is easy to produce many irrelevant images in the search results, which affects search accuracy. Additionally, the workload of manual labeling is large, and it is not easy to provide appropriate text labels for all the new pictures generated every day. The primary mechanism of content-based image retrieval technology is to extract various visual features of a query image [3]. A set of visually similar results to the query image is retrieved and returned to the user through feature matching with an image in a database. Content-based image retrieval technology works by matching visual image information, avoids the heavy workload of manual marking, andprovides retrieval results that can meet users' needs in terms of visual features. Because this method is based on the measurement of similarity between images, it can retrieve images similar to the target sample in the query image for the user. The accuracy of the results obtained by the image search engine query based on text annotation is often not ideal, so the research community favors content-based image retrieval. The visual characteristics of images such as color, texture, and shape are widely used for image indexing and retrieval. In recent years, various content-based image retrieval methods have sprung up. Color is the most used feature in image retrieval, and its definition is related to the color space used by the image. A color space [4] is a method of describing the space selected by a computer according to the needs of different scenes, including RGB, CIE 1931 XYZ color space, YUV, HSV, CMYK, and so on. The use of color features to describe images usually requires the combination of color distribution histogram technology. A statistical color distribution histogram structure is established to express color distribution and local spatial structure information by identifying the color information in a structure window. An HSV histogram [5] is used extensively in CBIR, offering better-colored images than grayscale images. Since human vision is more sensitive to brightness than to color intensity, the human visual system often uses the HSV color space, which is more in line with human visual characteristics than the RGB color space, to facilitate color processing and recognition. The modified color motif co-occurrence matrix (MCMCM) [6] collects the inter-correlation between the red, green, and blue color planes, absent in the color motif co-occurrence matrix. Typical color statistical features also include the color covariance matrix, color aggregation vector, color coherence vector (CCV) [7], and so on. Texture features can represent the internal spatial structure information of images. The most used is the LBP [8]; the standard LBP encodes the relationship between a reference pixel and its surrounding neighbors by comparing gray-level values.