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Comparative Analysis on Hybrid Content & Context-basedimage Retrieval System | ||
| Journal of Information Technology Management | ||
| دوره 13، Special Issue: Big Data Analytics and Management in Internet of Things، 2021، صفحه 133-142 اصل مقاله (957.82 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jitm.2021.80765 | ||
| نویسندگان | ||
| Arpana Mahajan1؛ Sanjay Chaudhary2؛ Sheshang Degadwala3 | ||
| 1Ph.D Research Scholar, Computer Engineering, Madhav University, Rajasthan, India. | ||
| 2Research Supervisor, Computer Engineering, Madhav University, Rajasthan, India. | ||
| 3Head of Department, Computer Engineering, Sigma Institute of Engineering, Vadodara, Gujarat, India. | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| CBIR؛ Content؛ Context؛ Machine learning؛ Deep learning | ||
| مراجع | ||
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