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An Improved Relevance Feedback in CBIR
June 21, 2020 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Subhadip Maji, Smarajit Bose
arXiv ID
2006.11821
Category
cs.IR: Information Retrieval
Cross-listed
stat.ML
Citations
1
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Relevance Feedback in Content-Based Image Retrieval is a method where the feedback of the performance is being used to improve itself. Prior works use feature re-weighting and classification techniques as the Relevance Feedback methods. This paper shows a novel addition to the prior methods to further improve the retrieval accuracy. In addition to all of these, the paper also shows a novel idea to even improve the 0-th iteration retrieval accuracy from the information of Relevance Feedback.
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