A novel plaid fabric image search method based on handcrafted features
Bibliographic record
Abstract
Purpose Fabric image search can obtain technological parameters of existing similar fabric images to guide production, saving lots of labor and material resources in the proofing process. Traditional fabric search methods require manual assistance, being time-consuming and subjective. Studying automatic search method based on feature engineering can improve the precision and efficiency of fabric retrieval. Design/methodology/approach This paper presents a novel image search method for plaid fabrics based on handcrafted features. First, local texture descriptors are extracted by the local binary pattern on the separated images which are processed by Fourier transform. Key-point texture descriptors are extracted by Scale-Invariant Feature Transform (SIFT) and Vector of Locally Aggregated Descriptors (VLAD). Second, color moments with image partitioning are extracted to characterize spatial color information of plaid fabric images, and color coherence vector is adopted to assist in characterizing spatial color features. Third, the similarities of the three features are calculated and fused by the weight assignment to realize the plaid fabric image retrieval. Findings To verify the proposed strategy, 44,000 fabric samples are collected from the factory to build the image database as the benchmark. Experiments show that precision and recall at rank five reach to 74.4 and 52.6%, respectively, and mAP reaches to 0.718. Results prove that the proposed strategy is feasible and effective, which can realize plaid fabric image search fast and efficiently. Originality/value Experiments prove the feasibility, effectiveness and superiority of the proposed method compared to existing methods. The proposed method can help the fabric manufacturing factory save lots of labor and material resources in the process of fabric design, manufacturing and sale.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".