Image retrieval based on swarm intelligence

International Journal of Electrical and Computer Engineering (IJECE) Vol. 11, No. 6, December 2021, pp. 5390~5401 ISSN: 2088-8708, DOI: 10.11591/ijece.v11i6.pp5390-5401  5390 Image retrieval based on swarm intelligence 1Department Shahbaa I. Khaleel1, Ragad W. Khaled2 of Software, College of Computer Science and Mathematics, Mosul University, Iraq 2Northern Technical University, Iraq Article Info ABSTRACT Article history: To keep pace with the development of modern technology in this information technology era, and the immense image databases, whether personal or commercial, are increasing, is requiring the management of these databases to strong and accurate systems to retrieve images with high efficiency. Because of the swarm intelligence algorithms are great importance in solving difficult problems and obtaining the best solutions. Here in this research, a proposed system is designed to retrieve color images based on swarm intelligence algorithms. Where the algorithm of the ant colony optimization (ACOM) and the intelligent water drop (IWDM) was used to improve the system's work by conducting the clustering process in these two methods on the features extracted by annular color moment method (ACM) to obtain clustered data, the amount of similarity between them and the query image, is calculated to retrieve images from the database, efficiently and in a short time. In addition, improving the work of these two methods by hybridizing them with fuzzy method, fuzzy gath geva clustering algorithm (FGCA) and obtaining two new high efficiency hybrid algorithms fuzzy ant colony optimization method (FACOM) and fuzzy intelligent water drop method (FIWDM) by retrieving images whose performance values are calculated by calculating the values of precision, recall and the f-measure. It proved its efficiency by comparing it with fuzzy method, FGCA and by methods of swarm intelligence without hybridization, and its work was excellent. Received Nov 6, 2020 Revised May 25, 2021 Accepted Jun 10, 2021 Keywords: Annular color moments Ant colony Image retrieval Intelligent water drop Swarm intelligence This is an open access article under the CC BY-SA license. Corresponding Author: Shahbaa I. Khaleel Department of Software College of Computer Science and Mathematics Mosul University, Iraq Email: 1. INTRODUCTION Due to the rapid growth of modern technology and the uses of the World Wide Web, as well as the development of multimedia technologies, interest has increased significantly over the past few years, with digital images and the way to access images stored within a massive database as soon as possible. Where similar images are queried from the stored image database by analyzing and extracting their contents so that their retrieval will be fast and accurate [1], [2]. In the past few years, several computer vision algorithms have been developed to represent images in minimal dimensions. Machine learning algorithms have also been used in the image retrieval process, as well as in feature extraction to classify the images according to them [3]. Image retrieval systems are a serious problem due to the huge amount of information that is searched for within a very large database. Therefore, it requires efficient and high-level techniques to ensure accurate retrieval of relevant information [4], [5]. Color is one of the most important visual features that represent low-level, as it can be relied upon to differentiate between visuals by the human eye. Therefore, the Journal homepage: http://ijece.iaescore.com Int J Elec & Comp Eng ISSN: 2088-8708  5391 extraction of color plays an important role in the representation of the characteristics of color images [6], [7]. In the image retrieval process, image content is dealt with, and most important features are color that enable humans to recognize images, so it is one of the common visual features used to recover color images, to ensure efficient performance of the retrieval process. There are many ways to extract features from images and they are efficient in the process of retrieving images from the database depending on the image of the query. The goal of feature extraction is to reduce the amount of data that is handled [8], [9]. Swarm based technologies have many applications within different specialties, and these technologies have been developed, and are different from one technology to another, and despite the difference, they implement the general form of these technologies. Most of these techniques have been modified based on the application used or the problem to be solved. Where the basis of work in all of them is to reach the optimal solution, that is, to reach the goal, and each technique has its own behavior to reach the solution in the search space [10]. Because of the technological development, the real world problems have become very complex, and to solve these problems with high quality, efficiency and accuracy, smart technologies have been used [11]. Clustering algorithms are used to collect data into groups, the similarity between the elements of one group is large, and the similarities between groups are few [12]. Fuzzy logic algorithms are used to perform the clustering operation and they perform much better than conventional clustering algorithms. This is because clustering algorithms have a membership function that contains membership degrees, which in turn performs a fine clustering [13]. In this research, a proposed system was designed to perform the image retrieval process based on the swarm intelligence algorithms. Where initially the features were extracted using the annular color moment method ACM method, the extraction of features is based on this method for its accuracy and obtain excellent characteristics that used in the process of retrieving color images. Where this method was applied to extract features and then its data were entries for clustering methods to ensure a retrieval of all images related to the database as per the query image provided. The clustering methods that used here are, the ant colony algorithm and intelligent water drop to perform the clustering process to reduce the time required to retrieve the images. The fuzzy clustering method fuzzy gath geva clustering algorithm (FGCA) was also used to clustered the database image features. In addition to hybridization of swarm intelligence methods with the fuzzy clustering algorithm to improve system performance. Here hybridized the FGCA with the ant colony optimization (ACOM) to produced a new method fuzzy ant colony optimization method (FACOM), and also proposed a novel method fuzzy intelligent water drop method (FIWDM). The remainder sections of the paper are organized as follows: Section 2 provides some related work on image retrieval. Section 3 explains the method for extracting the features that used here by the research. Section 4 describes the fuzzy clustering method, section 5 present the intelligent ant colony method (...truncated)