Feature Extraction and Image Retrieval of Landscape Images Based on Image Processing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it