{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T12:35:21Z","timestamp":1761395721863,"version":"3.40.5"},"reference-count":30,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2020,4,14]],"date-time":"2020-04-14T00:00:00Z","timestamp":1586822400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"name":"RUSA-Phase 2.0","award":["F. 24-51\/2014-U"],"award-info":[{"award-number":["F. 24-51\/2014-U"]}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Softw Pract Exp"],"published-print":{"date-parts":[[2021,3]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>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\u2010scale 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\u2010scale 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\u2010scale 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\u2009M and Oxford 5\u2009K 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\u2009minutes to complete the process, whereas the spark cluster requires a minimum of around only 19\u2009minutes to finish the process. The experimental outcome showed improvement in terms of various measures over the best rivals in the literature.<\/jats:p>","DOI":"10.1002\/spe.2834","type":"journal-article","created":{"date-parts":[[2020,4,14]],"date-time":"2020-04-14T07:44:32Z","timestamp":1586850272000},"page":"489-502","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["An efficient <scp>radix trie<\/scp>\u2010based semantic visual indexing model for <scp>large\u2010scale<\/scp> image retrieval in cloud environment"],"prefix":"10.1002","volume":"51","author":[{"given":"N.","family":"Krishnaraj","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering SASI Institute of Technology &amp; Engineering  Tadepalligudem India"}]},{"given":"Mohamed","family":"Elhoseny","sequence":"additional","affiliation":[{"name":"Faculty of Computers and Information Mansoura University  Mansoura Egypt"}]},{"given":"E. Laxmi","family":"Lydia","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering Vignan's Institute of Information Technology  Visakhapatnam India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2803-3846","authenticated-orcid":false,"given":"K.","family":"Shankar","sequence":"additional","affiliation":[{"name":"Department of Computer Applications Alagappa University  Karaikudi India"}]},{"given":"Omar","family":"ALDabbas","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering Al Balqa' Applied University  Salt Jordan"}]}],"member":"311","published-online":{"date-parts":[[2020,4,14]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2014.07.006"},{"key":"e_1_2_8_3_1","doi-asserted-by":"crossref","unstructured":"PhilbinJ ChumO IsardM SivicJ ZissermanA. Object retrieval with large vocabularies and fast spatial matching. 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