Content-Based Image Retrieval Using Composite Feature Vectors with Edge Features Based on Color and Pixel Similarity
Bibliographic record
Abstract
Content-based image retrieval involves searching for the desired image from an image database.It is realized using feature vectors obtained from the architectural image in question.Therefore, feature extraction is a crucial step.In this study, a novel feature vector representation method is proposed.In the proposed method, a composite feature vector is obtained by using color, edge, and gradient features.The most basic feature of the proposed method is that it uses the automatic pixel similarity approach for edge detection.The automatic pixel similarity approach offers a non-linear approach similar to the human visual system.Moreover, there is no need for any parameter or user intervention in edge detection.Additionally, the computational cost is much lower than those in many iterative non-linear edge detection approaches.In the study, experiments are carried out in the Corel-1K and Corel-10K databases, which are frequently used in image retrieval.The results of the proposed method are compared to those of 13 different methods.The superior performance of the proposed method is demonstrated.The high performance and low computational cost of the proposed method show that it can be easily implemented in many real-time image retrieval systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".