Feature Extraction and Image Retrieval of Landscape Images Based on Image Processing
Bibliographic record
Abstract
Facing the existing digital image libraries on landscape, researchers need to urgently solve a challenging problem: how to realize rational management and accurate retrieval of landscape images that contain feature information like hierarchy, layout, color system, and color matching. For accurate organization and labeling of landscape Images, this paper presents a novel method for feature extraction and image retrieval of landscape images based on image processing. Firstly, a color quantization process was designed for landscape images, and used to analyze the color composition and color space pattern (CSP) of such images. Next, the existing methods, which are suitable for the extraction of color features from landscape Images, were briefly reviewed, and the basic flows of our improved algorithm and division method of landscape color blocks (LCBs) were explained. Finally, the retrieval performance of landscape images was improved by matching of weighted color blocks of regional landscape, based on the multi-dimensional color eigenvectors of landscape image. The experimental results demonstrate the effectiveness of our algorithm. The research results shed light on the feature extraction from other types of color images.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".