Interactive mobile visual search for social activities completion using query image contextual model
Bibliographic record
Abstract
Mobile devices are ubiquitous. People use their phones as a personal concierge not only discovering information but also searching for particular interest on-the-go and making decisions. This brings a new horizon for multimedia retrieval on mobile. While existing efforts have predominantly focused on understanding textual or a voice query, this paper presents a new perspective which understands visual queries captured by the built-in camera such that mobile-based social activities can be recommended for users to complete. In this work, a query image-based contextual model is proposed for visual search. A mobile user can take a photo and naturally indicate an object-of-interest within the photo via circle based gesture called “O” gesture. Both selected object-of-interest region as well as surrounding visual context in photo are used in achieving a search-based recognition by retrieving similar images based on a large-scale of visual vocabulary tree. Consequently, social activities such as visiting contextually relevant entities (i.e., local businesses) are recommended to the users based on their visual queries and GPS location. Along with the proposed method, an exemplary real application has been developed on Windows Phone 7 devices and evaluated with a wide variety of scenarios on million-scale image database. To test the performance of proposed mobile visual search model, extensive experimentation has been conducted and compared with state-of-the-art algorithms in content-based image retrieval (CBIR) domain.
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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 | 0.004 |
| 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".