With the recent development of real-time image processing, technologies for object recognition and object retrieval in images extracted from real-time operating devices, such as closed-circuit television (CCTV), are being actively implemented [1,2,3,4,5,6,7]. These technologies can be used in various fields, such as crime prevention, monitoring systems, and analyzing traffic information.

Content-based image retrieval (CBIR) involves the retrieving of images by using features extracted from objects in video images. CBIR involves vectorizing the features extracted from images and determining the similarities between the vectors to retrieve similar images. In sum, CBIR is a way of retrieving images from a database. In CBIR, a user specifies a query image and obtains images from the database that are similar to the query. To find the most similar images, CBIR compares the content of the query image with the database images. Owing to the development of new technologies, such as artificial intelligence and machine learning, researchers have conducted studies into extracting various features from images [8,9,10,11]. Data becomes increasingly high-dimensional as the features provided by images become more varied. Therefore, similarity retrieval techniques that use high-dimensional data are needed to retrieve and compare similar images or objects. Additionally, the indexing structure for high-dimensional data similarity retrieval must be appropriately constructed, in accordance with the characteristics of high-dimensional data.

Nearest neighbor search (NNS) deals with the problem of finding the closest or most similar item to a given point. Closeness is typically expressed in terms of a dissimilarity function, such as Euclidean distance, Manhattan distance, and other distance metrics. Formally, the nearest-neighbor (NN) search problem is defined as follows: given a set S of points in a space M and a query point q ∈ M, find the closest point in S to q. k-NN search is a generalized NN problem. k-NN needs to find the k closest points.

Generally, in the distributed processing environment, a distributed k-NN query is processed as follows. First, each distributed node indexes data points. Depending on the indexing scheme, the k- NN query processing methodology is different. In general, when a k-NN query is inputted, all of the nodes are requested to process the k-NN query. Each node generates k closest data points and k data points generated in n nodes are merged. Finally, the result includes the k closest points to the query from the merged result.

Various studies have been conducted recently to address these problems [12,13,14,15,16,17]. A distributed, in-memory-based, high-dimensional indexing technique was proposed to efficiently perform image retrieval using high-dimensional feature data [12]. The authors of [12] utilized big data analysis platforms, such as Spark, to implement distributed, in-memory-based, high-dimensional indexing. In addition, the authors of [13] proposed an M-tree [13] indexing algorithm on Spark. Since all distributed servers participate in query processing [12], the load on all the servers can increase when there are many retrieval requests from users. In another study, a master/slave model for distributed index query processing was used to perform efficient image retrieval in airport video monitoring systems [14]. The researchers proposed a distributed MVP (multi-vantage-point) tree, which was based on the MVP tree. However, it had an inherent flaw: it was difficult to load large-scale, high-dimensional data into its memory. Moreover, backtracking operations frequently occurred when performing k-NN query processing in the tree. Backtracking is a commonly used algorithm for searching. When processing k-NN queries in a tree structure, it explores a specific node and returns to the parent node if there are no results. It generates query results by repeating this process. In another study, a distributed k-d tree [16] was proposed to enhance the performance of k-NN processing [15]. However, depending on the amount of distributed data, the height of the k-d tree could increase, which would increase the search time [15]. Using a k-d tree also results in frequent backtracking operations when performing k-NN processing [14].

Contrary to conventional hash functions [17], LSH (locality-sensitive hashing) aims to maximize hash collisions when indexing high-dimensional data. It stores similar data in the same bucket to improve the efficiency of the search and indexing. Using random vectors, LSH transforms high-dimensional data into low-dimensional bucket indexes. Following a query request, the system searches for the bucket that contains the query result using the query location, measures the actual distance between the data within the bucket, and performs k-NN query processing. However, in LSH, the k-NN results may include false positives, depending on the index creation parameters. More buckets can be searched to ensure accurate results; however, this increases the search cost because it requires distance comparisons among all the data in the adjacent buckets. Because k-NN query processing involves finding the closest k items, distance-based indexing is efficient as it can pre-calculate the distance values to the items and index them.

The authors of [18] used a deep learning technique based on a combination of CNNs to classify images, and used RNNs to analyze natural language queries. They also used CNNs to take advantage of the deep learning technology in image content classification. The RNN model helps users to make search queries more efficiently. The authors of [19] identified occupied and vacant parking lots using a hybrid deep learning model. The model proposed in that study combined the superior features of CNNs and LSTM deep learning methods. The authors of [20] proposed a CBIR system that was based on multi deep neural networks, which combined convolutional neural networks (CNNs) and k-NN methodologies. The feature extraction of the user-supplied image was performed based on the CNNs, and the image similarity was calculated based on the k-NN methodologies in order to return a list of images. The authors of [21] compared the image retrieval of various machine learning models, such as SVMs (support vector machines), k-NNs, and CNNs.

2. k-NN Query Optimization for High-Dimensional Index Using Machine Learning