Improved Deep Perceptual Hashing Algorithm (IDP-HA), Information Retrieval System, Microsoft Common Objects In Context (MS COCO), Remote Cloud Computing, Computer science, information systems
DOI:
https://doi.org/10.21123/bsj.2024.11089Keywords:
Improved Deep Perceptual Hashing Algorithm, Information Retrieval System, Remote Cloud ComputingAbstract
The growth of information retrieval and associated services can be attributed to technical advancements. Meanwhile, traditional information retrieval methods are impacted by performance, accuracy, and scalability limitations. An information retrieval system for distant cloud computing that is based on an Improved Deep Perceptual Hashing Algorithm (IDP-HA) is one of the solutions that have been developed to solve these constraints. Systems are widely used due to their ability to recognize intricate patterns in data. The accuracy of information similarity measurement is still lacking due to the inherent complexity of data and measuring methods. The deep perceptual hashing approach uses Deep Neural Network (DNN) frameworks to extract hierarchical features from input images from the Microsoft Common Objects in Context (MS COCO) dataset. The Gaussian filter (GF) is a tool used in the pre-processing of individual images for various computer visions. Subsequently, this method generates digital hash numbers by describing the visual elements of the images using a threshold mechanism. Its primary goal is to improve a similarity metric to maintain perceptual similarity and guarantee that hash codes for visually comparable images are similar. Memory usage is decreased by using the hash function as the first step in establishing a connection between the database and the query. The approach finds applications in content-based image retrieval systems, image retrieval, picture clustering, and copy detection. Overall, it offers a strong framework for producing compact and semantically significant image representations. The IDP-HA has been enhanced for remote cloud computing to boost theaverage recall, average Precision, and average F1 Measurement and average query timing of data retrieval processes. The method reduces latency and increases system efficiency by generating compact binary representations of multimedia data. Retrieval based on visual similarity can be dependable and natural since perceptual similarity is maintained.
Received 05/03/2024
Revised 13/10/2024
Accepted 15/10/2024
Published Online First 20/12/2024
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