Comparative Analysis on Hybrid Content & Context-basedimage Retrieval System
Learning effective segment depictions and resemblance measures are fundamental to the recuperation execution of a substance based picture recuperation (CBIR) structure. Regardless of wide research tries for a significant long time, it stays one of the most testing open gives that broadly impedes the...
| Main Authors: | , , |
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| Format: | Article |
| Language: | fas |
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University of Tehran
2021-05-01
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| Series: | Journal of Information Technology Management |
| Subjects: | |
| Online Access: | https://jitm.ut.ac.ir/article_80765_3952c04ba80190d16e580170081e7bd7.pdf |
| _version_ | 1818902494912184320 |
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| author | Arpana Mahajan Sanjay Chaudhary Sheshang Degadwala |
| author_facet | Arpana Mahajan Sanjay Chaudhary Sheshang Degadwala |
| author_sort | Arpana Mahajan |
| collection | DOAJ |
| description | Learning effective segment depictions and resemblance measures are fundamental to the recuperation execution of a substance based picture recuperation (CBIR) structure. Regardless of wide research tries for a significant long time, it stays one of the most testing open gives that broadly impedes the achievements of real-world CBIR structures. The key test has been credited to the extraordinary "semantic hole" subject that happens between low-level photo pixels got by technologies and raised close semantic thoughts saw by a human. Among various techniques, AI has been successfully analyzed as a possible course to interface the semantic gap in the whole deal. Impelled by late triumphs of significant learning techniques for PC vision and various applications, in this paper, we try to address an open issue: if significant learning is a longing for spreading over the semantic gap in CBIR and how much updates in CBIR endeavors can be cultivated by exploring the front line significant learning methodology for learning feature depictions and likeness measures. Specifically, we explore a structure of significant learning with application to CBIR assignments with a wide game plan of definite examinations by investigating front line significant learning methodologies for CBIR endeavors under moved settings. From our exploratory examinations, we find some encouraging results and compress some huge bits of information for upcoming research. |
| first_indexed | 2024-12-19T20:36:33Z |
| format | Article |
| id | doaj.art-32f033670a05418bb482f66f9bb58925 |
| institution | Directory Open Access Journal |
| issn | 2008-5893 2423-5059 |
| language | fas |
| last_indexed | 2024-12-19T20:36:33Z |
| publishDate | 2021-05-01 |
| publisher | University of Tehran |
| record_format | Article |
| series | Journal of Information Technology Management |
| spelling | doaj.art-32f033670a05418bb482f66f9bb589252022-12-21T20:06:30ZfasUniversity of TehranJournal of Information Technology Management2008-58932423-50592021-05-0113Special Issue: Big Data Analytics and Management in Internet of Things13314210.22059/jitm.2021.8076580765Comparative Analysis on Hybrid Content & Context-basedimage Retrieval SystemArpana Mahajan0Sanjay Chaudhary1Sheshang Degadwala2Ph.D Research Scholar, Computer Engineering, Madhav University, Rajasthan, India.Research Supervisor, Computer Engineering, Madhav University, Rajasthan, India.Head of Department, Computer Engineering, Sigma Institute of Engineering, Vadodara, Gujarat, India.Learning effective segment depictions and resemblance measures are fundamental to the recuperation execution of a substance based picture recuperation (CBIR) structure. Regardless of wide research tries for a significant long time, it stays one of the most testing open gives that broadly impedes the achievements of real-world CBIR structures. The key test has been credited to the extraordinary "semantic hole" subject that happens between low-level photo pixels got by technologies and raised close semantic thoughts saw by a human. Among various techniques, AI has been successfully analyzed as a possible course to interface the semantic gap in the whole deal. Impelled by late triumphs of significant learning techniques for PC vision and various applications, in this paper, we try to address an open issue: if significant learning is a longing for spreading over the semantic gap in CBIR and how much updates in CBIR endeavors can be cultivated by exploring the front line significant learning methodology for learning feature depictions and likeness measures. Specifically, we explore a structure of significant learning with application to CBIR assignments with a wide game plan of definite examinations by investigating front line significant learning methodologies for CBIR endeavors under moved settings. From our exploratory examinations, we find some encouraging results and compress some huge bits of information for upcoming research.https://jitm.ut.ac.ir/article_80765_3952c04ba80190d16e580170081e7bd7.pdfcbircontentcontextmachine learningdeep learning |
| spellingShingle | Arpana Mahajan Sanjay Chaudhary Sheshang Degadwala Comparative Analysis on Hybrid Content & Context-basedimage Retrieval System Journal of Information Technology Management cbir content context machine learning deep learning |
| title | Comparative Analysis on Hybrid Content & Context-basedimage Retrieval System |
| title_full | Comparative Analysis on Hybrid Content & Context-basedimage Retrieval System |
| title_fullStr | Comparative Analysis on Hybrid Content & Context-basedimage Retrieval System |
| title_full_unstemmed | Comparative Analysis on Hybrid Content & Context-basedimage Retrieval System |
| title_short | Comparative Analysis on Hybrid Content & Context-basedimage Retrieval System |
| title_sort | comparative analysis on hybrid content context basedimage retrieval system |
| topic | cbir content context machine learning deep learning |
| url | https://jitm.ut.ac.ir/article_80765_3952c04ba80190d16e580170081e7bd7.pdf |
| work_keys_str_mv | AT arpanamahajan comparativeanalysisonhybridcontentcontextbasedimageretrievalsystem AT sanjaychaudhary comparativeanalysisonhybridcontentcontextbasedimageretrievalsystem AT sheshangdegadwala comparativeanalysisonhybridcontentcontextbasedimageretrievalsystem |