Improved Content Based Image Retrieval Process Based on Deep Convolutional Neural Network and Salp Swarm Algorithm

Abstract

Digital image and medical image retrieval from several repositories are improving gradually, so the capacity of repositories increases rapidly. The semantic space is the main issue on content-based image retrieval (CBIR), which exists among the semantic level as well as increases the data recognized through human and low level visible data obtained through the image. The CBIR system utilizes the deep convolutional neural network (DCNN), which is trained to medical image characterization and the digital image by salp swarm optimization algorithm (SSA). The average classification accuracy for medical image is 86.805%, a mean average precision is 79%, Average Recall Rate (ARR) is 91.7% and [Formula: see text]-measure is 84.9%, are achieved during retrieval task. For image retrieval, the Average Precision Rate (APR) improved from 39%, 40%, 36% and 42.5% to 86.8% and the ARR enhanced from 39.5%, 40.5%, 35.5% and 42.5% to 86.8%. The [Formula: see text]-measure is improved from 39.5%, 40.5%, 35.5% and 42.5% to 86.8% as different with Local tetra patterns (LTrP), LOOP, local derivative pattern (LDP) and local mean differential excitation pattern (LMDeP) separately on Corel-1K dataset. The presented method is most suitable for multimodal digital images and medical image retrieval for various parts of the body.