Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
97 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

An Improved Relevance Feedback in CBIR (2006.11821v2)

Published 21 Jun 2020 in cs.IR and stat.ML

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.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (2)
  1. Subhadip Maji (11 papers)
  2. Smarajit Bose (8 papers)
Citations (1)

Summary

We haven't generated a summary for this paper yet.