Evolutionary Optimization Algorithm on Content based Image Retrieval System using Handcrafted features with Squeeze Networks

Content based image retrieval (CBIR) is commonly utilized in several application areas due to the rising significance of images in day to day lives. On comparing with textual data, images require high storage area and processing complexity. The latest advances in machine (ML) and deep learning (DL) models can be utilized for the design of effective CBIR In this view, this paper presents an evolutionary optimization algorithm on CBIR system using handcrafted features with squeeze networks (EOCBIR-HFSN) technique. The goal of the EOCBIR-HFSN technique is to proficiently retrieve the related images based on the query image (Q1). The proposed EOCBIR-HFSN technique involves the feature extraction process by the use of local binary patterns (LBP) based handcrafted features and SqueezeNet based deep features. Besides, the hyper-parameter tuning of the SqueezeNet model is performed by the grasshopper optimization algorithm (GOA), shows the novelty of the work. Finally, Euclidean distance metric is used to determine the highly similar images from the database. The comprehensive result analysis of the EOCBIR-HFSN technique take place on benchmark database reported the enhanced outcomes over the other techniques.