Computer Image Content Retrieval considering K-Means Clustering Algorithm

Abstract

The traditional computer image content retrieval technology can only meet the specific requirements of customers; because of its general features, it cannot comply with the requirements of various environments, purposes, and time simultaneously. This study presents a computer image content retrieval method for a K-means clustering algorithm (KCA). The information collected by computer is preprocessed by K-means clustering algorithm, and the unacquired computer image is labeled based on the optimal learning order according to the KCA. The K-means clustering algorithm classifies the color, pattern, shape, and content of computer images and takes advantage of the invariance advantages of image content retrieval such as scale, rotation, illumination, and blur correction to effectively solve the recurring problems of computer images during retrieval and increase its accuracy. The results of the experiment indicate that the proposed K-means clustering algorithm can enhance the efficiency and performance effectively during image retrieval by computer as compared to the traditional content search algorithm and also help to quickly converge to the query content; it also shows that KCA has good robustness.

Evaluation and Analysis Model of Economic Development Level for Latin American Countries

“…Many metrics can be used to evaluate the performance of clustering models in extrinsic methods. Entropy and F-measure are typical representatives [24][25][26]: Entropy: it indicates the degree of chaos in the system; the greater the entropy, the more chaotic the system. e specific calculation process of the entropy of each cluster i is shown as follows, where L is the number of classes, p ij � m ij /m i represents the probability that the data in cluster i belongs to class j, m i represents the number of datasets in cluster i, and m ij is the sum of the number of values in class j in cluster i, e represents the total entropy of all clusters, K represents the sum of the number of clusters, and m represents the total number of datasets.…”

A Novel Deep Learning-Based Visual Search Engine in Digital Marketing for Tourism E-Commerce Platforms

“…In this algorithm, the fused single feature descriptors are computed by proficient fusion of color, texture, and shape feature moments. Model 6: Yu and Liu (2022) proposed an image content retrieval method for a K-means clustering algorithm (KCA). The K-means clustering algorithm in this study is used to classify the color, pattern, shape, and content of images.…”

Wheat Lodging Direction Detection for Combine Harvesters Based on Improved K-Means and Bag of Visual Words

“…There are calculation errors of the same spatial point coordinate, so the threshold (x t , y t ) for gross error elimination can be calculated based on the mean (∆x, ∆y) of the coordinate deviation. According to Equations ( 29) and (30), the corrected IPM coordinates (x wi , y wi ) are calculated. The wheat lodging direction detection in the field is mainly used for the real-time operation control of harvesters, so the region of interest for detection is within a certain range of the front of the harvester.…”

“…For the accuracy and efficiency requirements of vehicle vision detection, the k-means algorithm and SVM model with good real-time and generalization performance are adopted. Due to the manual setting of the initial value, random initial clustering centers, limited features, and redundant distance calculations [30,31], the clustering accuracy and efficiency of the existing k-means algorithm do not easily meet the requirements of vehicle lodging detection. A cluster validity evaluation function and multichannel and multidimensional feature vectors are constructed to improve the k-means algorithm.…”