Perceptual Image Hashing Using Random Forest for Content-based Image Retrieval
Bibliographic record
Abstract
Use of large image datasets has become a common occurrence. This, however, makes image searching a highly desired operation in many applications. Most of the content-based image retrieval (CBIR) methods usually adopt machine-learning techniques that take the image content into account. These methods are effective, but they are generally too complex and resource demanding. We propose a framework based on image hashing and random forest, which is fast and offers high performance. The proposed framework consists of a multi-key image hashing technique based on discrete cosine transform (DCT) and discrete wavelet transform (DWT) and random forest based on normalized B+ Tree (NB+ Tree), which reduces the high-dimensional input vectors to one-dimension, which in turn improves the time complexity significantly. We analyze our method empirically and show that it outperforms competitive methods in terms of both accuracy and speed. In addition, the proposed scheme maintains a fast scaling with increasing size of the data sets while preserving high accuracy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".