Automatic Website Summarization by Image Content: A Case Study with Logo and Trademark Images
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
Image-based abstraction (or summarization) of a Web site is the process of extracting the most characteristic (or important) images from it. The criteria for measuring the importance of images in Web sites are based on their frequency of occurrence, characteristics of their content and Web link information. As a case study, this work focuses on logo and trademark images. These are important characteristic signs of corporate Web sites or of products presented there. The proposed method incorporates machine learning for distinguishing logo and trademarks from images of other categories (e.g., landscapes, faces). Because the same logo or trademark may appear many times in various forms within the same Web site, duplicates are detected and only unique logo and trademark images are extracted. These images are then ranked by importance taking frequency of occurrence, image content and Web link information into account. The most important logos and trademarks are finally selected to form the image-based summary of a Web site. Evaluation results of the method on real Web sites are also presented. The method has been implemented and integrated into a fully automated image-based summarization system which is accessible on the Web (www.intelligence.tuc.gr/websummarization)
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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