Combining Dense Features with Interest Regions for Efficient Part-based Image Matching
Bibliographic record
Abstract
One of the most popular approaches for object recognition is bag-of-words which represents an image as a histogram of the frequency of occurrence of visual words. But it has some disadvantages. Besides requiring computationally expensive geometric verification to compensate for the lack of spatial information in the representation, it is particularly unsuitable for sub-image retrieval problems because any noise, background clutter or other objects in vicinity influence the histogram representation. In our previous work, we addressed this issue by developing a novel part-based image matching framework that utilizes spatial layout of dense features within interest regions to vastly improve recognition rates for landmarks. In this paper, we improve upon the previously published recognition results by more than 12% and achieve significant reductions in computation time. A region of interest (ROI) selection strategy is proposed along with a new voting mechanism for ROIs. Also, inverse document frequency weighting is introduced in our image matching framework for both ROIs and dense features inside the ROIs. We provide experimental results for various vocabulary sizes on the benchmark Oxford 5K and INRIA Holidays datasets.
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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 | 0.000 | 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 | 0.000 |
| 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 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".