TY - JOUR ID - 87122 TI - A Modified Grasshopper Optimization Algorithm Combined with CNN for Content Based Image Retrieval JO - International Journal of Engineering JA - IJE LA - en SN - 1025-2495 AU - Sezavar, A. AU - Farsi, H. AU - Mohamadzadeh, Sajad AD - Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran Y1 - 2019 PY - 2019 VL - 32 IS - 7 SP - 924 EP - 930 KW - Content-based image retrieval KW - Deep Learning KW - convolutional neural network KW - Grasshopper optimization DO - 10.5829/ije.2019.32.07a.04 N2 - Nowadays, with huge progress in digital imaging, new image processing methods are needed to manage digital images stored on disks. Image retrieval has been one of the most challengeable fields in digital image processing which means searching in a big database in order to represent similar images to the query image. Although many efficient researches have been performed for this topic so far, there is a semantic gap between human concept and features extracted from the images and it has become an important problem which decreases retrieval precision. In this paper, a convolutional neural network (CNN) is used to extract deep and high-level features from the images. Next, an optimization problem is defined in order to model the retrieval system. Heuristic algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) have shown an effective role in solving the complex problems. A recent introduced heuristic algorithm is Grasshopper Optimization Algorithm (GOA) which has been proved to be able to solve difficult optimization problems. So, a new search method, modified grasshopper optimization algorithm (MGOA) is proposed to solve modeled problem and to retrieve similar images efficiently, despite of total search in database. Experimental results showed that the proposed system named CNN-MGOA achieves superior accuracy compared to traditional methods. UR - https://www.ije.ir/article_87122.html L1 - https://www.ije.ir/article_87122_c98ece12fa7377fe34ea6c8a5f519352.pdf ER -