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An efficient radix trie ‐based semantic visual indexing model for large‐scale image retrieval in cloud environment
Author(s) -
Krishnaraj N.,
Elhoseny Mohamed,
Lydia E. Laxmi,
Shankar K.,
ALDabbas Omar
Publication year - 2021
Publication title -
software: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 70
eISSN - 1097-024X
pISSN - 0038-0644
DOI - 10.1002/spe.2834
Subject(s) - computer science , search engine indexing , spark (programming language) , cloud computing , information retrieval , scale (ratio) , image retrieval , semantics (computer science) , data mining , artificial intelligence , image (mathematics) , physics , quantum mechanics , programming language , operating system
Summary In recent years, massive growth in the number of images on the web has raised the requirement of developing an effective indexing model to search digital images from a large‐scale database. Though cloud service offers effective indexing of compressed images, it remains a major issue due to the semantic gap between the user query and diverse semantics of large‐scale database. This article presents a radix trie indexing (RTI) model based on semantic visual indexing for retrieving the images from cloud platforms. Initially, an interactive optimization model is applied to identify the joint semantic and visual descriptor space. Next, an RTI model is applied to integrate the semantic visual joint space model for finding an effective solution for searching large‐scale sized dataset. Finally, a Spark distributed model is applied for deploying the online image retrieval service. The performance of the proposed method is validated on two standard dataset, namely, Holidays 1 M and Oxford 5 K in terms of mean average precision (mAP) and processing time under varying dataset sizes. During experimentation, the presented RTI model shows the maximum mAP value of 0.83 under the dataset size of 1000. Similarly, under the sample count of 1000, it is noted that the standalone server requires a maximum of 118 minutes to complete the process, whereas the spark cluster requires a minimum of around only 19 minutes to finish the process. The experimental outcome showed improvement in terms of various measures over the best rivals in the literature.