A Two-Stage Outdoor - Indoor Scene Classification Framework: Experimental Study for the Outdoor Stage
Bibliographic record
Abstract
The state-of-the-art image classification methods require an intensive learning stage and a considerable amount of training images. Recently, with the introduction of these models (and in particular convolutional neural network (CNN)), it is believed that the best solution to achieve a system with high performance on scene classification is to learn deep scene features using CNN. While this can be true for large-scale image datasets (in the scale of one-million training images), the feasibility of reaching state-of-the-art performances in other complex datasets with a handful training examples remains unclear. Here, we argue that we can reach a highly accurate performance by presenting a simple model on the scene classification problem. This paper presents a hierarchical two-stage scene classification framework based on indoor versus outdoor notion. The suggested approach is a simple, yet a very efficient global image representation model for scene recognition. Though the term of indoor versus outdoor has been in the literature for quite a while, here we go further than that and propose a scene classifier model which classifies all the outdoor scene classes versus one undifferentiated indoor class. To achieve this, we tackle the problem of scene classification by using distributions of quantized filter responses to characterize the scenes. Later the classifier outputs either one of the several outdoor scene classes or a generic indoor scene class. We validate the proposed approach by feeding the algorithm with two benchmark datasets: 15-Scene category and SUN397. Successful experiments demonstrate the validity of the proposed approach. With the presented model, we reach an average accuracy of 97.84% and 55.1% in outdoor scenes and 93.09% and 92.4% in the indoor scene class on 15-Scene and SUN397 respectively.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".