Content-based image retrieval based on supervised learning and statistical-based moments

Abstract

Content-based image retrieval (CBIR) system generally retrieves images based on the matching of the query image from all the images of the database. This exhaustive matching and searching slow down the image retrieval process. In this paper, a fast and effective CBIR system is proposed which uses supervised learning-based image management and retrieval techniques. It utilizes machine learning approaches as a prior step for speeding up image retrieval in the large database. For the implementation of this, first, we extract statistical moments and the orthogonal-combination of local binary patterns (OC-LBP)-based computationally light weighted color and texture features. Further, using some ground truth annotation of images, we have trained the multi-class support vector machine (SVM) classifier. This classifier works as a manager and categorizes the remaining images into different libraries. However, at the query time, the same features are extracted and fed to the SVM classifier. SVM detects the class of query and searching is narrowed down to the corresponding library. This supervised model with weighted Euclidean Distance (ED) filters out maximum irrelevant images and speeds up the searching time. This work is evaluated and compared with the conventional model of the CBIR system on two benchmark databases, and it is found that the proposed work is significantly encouraging in terms of retrieval accuracy and response time for the same set of used features.

An Intrusion Detection Method Based on Decision Tree-Recursive Feature Elimination in Ensemble Learning

“…e method uses an automatic clustering algorithm to find the clusters with the maximum similarity between the proposed cluster elements and the smallest similarity with other clusters. e supervised learning 2 Mathematical Problems in Engineering method requires a lot of label data [13]. But, the unsupervised learning method can automatically cluster without a large amount of labeled data.…”

Design of Quick Search Method for Key Feature Images in Mobile Networks

“…In order to test the method proposed in this paper, the key features of rotating invariant local binary mode images are used. Methods in reference [8] and reference [9] were selected as comparison methods to study the image retrieval performance of key features under different complex backgrounds together with the method in this paper. The statistical results are shown in Table 2.…”

“…Although this method can retrieve images with high quality, the retrieval speed is too slow. Singer et al proposed a content image retrieval method based on supervised learning and statistical moments [9], which can complete image retrieval faster, but the accuracy of image retrieval is poor. This paper studies the quick search of key feature images in mobile networks.…”

Multipopulation Genetic Algorithms with Different Interaction Structures to Solve Flexible Job-Shop Scheduling Problems: A Network Science Perspective

“…e optimal value, mentioned in this paper, is defined as the best value found by all current algorithms by now, rather than the best value found by the currently running algorithm. Also, due to the stochastic nature of MPGA, we have to run the proposed algorithm repeatedly, and statistical features are often utilized to analyze simulation results [38][39][40].…”