Pith. Sign in

RECORD

An Improved Relevance Feedback in CBIR

PaperPDF from arXiv

data files

Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
X Bluesky LinkedIn Reddit Hacker News
Subhadip Maji, Smarajit Bose

Information Retrieval · Submitted 21 Jun 2020

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim This paper shows a novel addition to the prior methods to further improve the retrieval accuracy.

how it is stated · asserted 1.00 read the record →

Comment (0) Read on arXiv ↗
Cite ▾
arXiv ID2006.11821 (version 2)
Pith IDNMQNTYEI, Pith's permanent record of this paper and its review
CitationSmarajit Bose, Subhadip Maji. "An Improved Relevance Feedback in CBIR." arXiv:2006.11821 (2020). https://pith.science/paper/NMQNTYEI
Claim paper

The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Open the compiled record

Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,513 input tokens.

What the abstract itself states, as probabilities: claims first 0.14 · states limitation 0.05 · human participants 0.14 · controlled comparison 0.18 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.28 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Relevance Feedback in Content-Based Image Retrieval is a method where the feedback of the performance is being used to improve itself. background 1.00 0.06 0.04 asserted 1.00
s1 Prior works use feature re-weighting and classification techniques as the Relevance Feedback methods. background 1.00 0.03 0.03 asserted 1.00
s2 This paper shows a novel addition to the prior methods to further improve the retrieval accuracy. contribution 1.00 0.85 0.30 asserted 1.00
s3 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. contribution 1.00 0.89 0.40 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.51.

What was examined, and what was not

sources read
  • arXiv listing abstract v2
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original 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.

Human review

How Pith checks papers → · Full audit of this paper →

Discussion

Know something about this paper? Leave a comment, or highlight any passage above to comment on it.