Abstract
Metric learning has been widely employed, especially in various computer vision tasks, with the fundamental assumption that all samples (e.g., regions/superpixels in images/videos) are independent and identically distributed (IID). However, since the samples are usually spatially-connected or temporally-correlated with their physically-connected neighbours, they are not IID (non-IID for short), which cannot be directly handled by existing methods. Thus, we propose to learn and integrate non-IID metrics (NIME). To incorporate the non-IID spatial/temporal relations, instead of directly using non-IID features and metric learning as previous methods, NIME first builds several non-IID representations on original (non-IID) features by various graph kernel functions, and then automatically learns the metric under the best combination of various non-IID representations. NIME is applied to solve two typical computer vision tasks: interactive image segmentation and histology image identification. The results show that learning and integrating non-IID metrics improves the performance, compared to the IID methods. Moreover, our method achieves results comparable or better than that of the state-of-the-arts.
Original language | English |
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Pages | 1524-1531 |
Number of pages | 8 |
Publication status | Published - 2017 |
Event | 31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States Duration: 2017 Feb 4 → 2017 Feb 10 |
Other
Other | 31st AAAI Conference on Artificial Intelligence, AAAI 2017 |
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Country/Territory | United States |
City | San Francisco |
Period | 17/2/4 → 17/2/10 |
ASJC Scopus subject areas
- Artificial Intelligence