Open Access

ARTICLE

An Efficient Content-Based Image Retrieval System Using kNN and Fuzzy Mathematical Algorithm

Chunjing Wang^*, Li Liu, Yanyan Tan^*

School of Information Science and Engineering, Shandong Normal University, Jinan, 250014, China

* Corresponding Author: Chunjing Wang. Email:

(This article belongs to the Special Issue: Security Enhancement of Image Recognition System in IoT based Smart Cities)

Computer Modeling in Engineering & Sciences 2020, 124(3), 1061-1083. https://doi.org/10.32604/cmes.2020.010198

Received 16 February 2020; Accepted 02 June 2020; Issue published 21 August 2020

Abstract

The implementation of content-based image retrieval (CBIR) mainly depends on two key technologies: image feature extraction and image feature matching. In this paper, we extract the color features based on Global Color Histogram (GCH) and texture features based on Gray Level Co-occurrence Matrix (GLCM). In order to obtain the effective and representative features of the image, we adopt the fuzzy mathematical algorithm in the process of color feature extraction and texture feature extraction respectively. And we combine the fuzzy color feature vector with the fuzzy texture feature vector to form the comprehensive fuzzy feature vector of the image according to a certain way. Image feature matching mainly depends on the similarity between two image feature vectors. In this paper, we propose a novel similarity measure method based on k-Nearest Neighbors (kNN) and fuzzy mathematical algorithm (SBkNNF). Finding out the k nearest neighborhood images of the query image from the image data set according to an appropriate similarity measure method. Using the k similarity values between the query image and its k neighborhood images to constitute the new k-dimensional fuzzy feature vector corresponding to the query image. And using the k similarity values between the retrieved image and the k neighborhood images of the query image to constitute the new k-dimensional fuzzy feature vector corresponding to the retrieved image. Calculating the similarity between the two kdimensional fuzzy feature vector according to a certain fuzzy similarity algorithm to measure the similarity between the query image and the retrieved image. Extensive experiments are carried out on three data sets: WANG data set, Corel-5k data set and Corel-10k data set. The experimental results show that the outperforming retrieval performance of our proposed CBIR system with the other CBIR systems.

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Keywords

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