Advertisement Image Classification—Visual ( <scp>RESNET</scp> ) Versus Textual ( <scp>BERT</scp> ) Features: An Experimental Study
Bibliographic record
Abstract
ABSTRACT Newspapers serve as a vital source for various types of advertisements. Individuals eagerly await and search for advertisements relevant to them in newspapers. However, both printed newspapers and online newspapers lack the ability to provide category‐wise advertisement search options. As a result, searching a newspaper advertisement in a specific category becomes very time‐consuming and cumbersome due to sequential manual search across multiple newspapers. To address this problem in online newspapers, a classification model is needed that can classify advertisement images into predefined categories and hence allow users to perform category‐wise advertisement searches with much ease. This research introduces and compares two sets of classification models for advertisement images in online English newspapers in India. The first set utilizes visual features to train seven different classification models by fine‐tuning different layers of the Residual Network with 50 layers (ResNet50) pretrained model and achieves a maximum classification accuracy of 71.41%. The second set utilizes textual features to train 14 different classification models by fine‐tuning different layers of the pretrained Bidirectional Encoder Representations from Transformers (BERT) base model and achieves maximum classification accuracies in the range from 96.88% to 97.34%. This significant enhancement of more than 25% underscores the superiority of textual features over visual ones in understanding Indian online English newspaper advertisement images and holds promise for practical applications, including categorized advertisement searches.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".