Image Feature Extraction and Retrieval Optimization of Book Pages Based on Convolutional Neural Network
Bibliographic record
Abstract
Focusing on the feature extraction process of convolutional neural network (CNN), this paper establishes a CNN-based retrieval method of book pages. Then, the pretraining and feature finetuning of the CNN were described separately. The performance of the proposed optimization method was demonstrated through experiments. Considering overall performance and transfer learning capacity, the eight-layer VGG-Fast was selected as the structural framework of our CNN. To train the CNN, it is necessary to gather millions of book page images, and complete the complex task of labeling all these images. Given the excellence of VGG in many transfer learning tasks, this paper chooses to pretrain the CNN with a task-independent dataset. After that, a small book page dataset was adopted to convert the knowledge domain of the CNN from image classification to image page retrieval. In this way, desirable retrieval effects were achieved, without wasting lots of time and energy in collecting and labeling a large book page dataset.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".