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Discriminative Segment Annotation in Weakly Labeled Video
Kevin Tang, Rahul Sukthankar
Appeared in CVPR 2013 (Oral)
Research Problem• Input: a weakly labeled video (eg., “dog”)• Output: identify segments that correspond to the label to generate the semantic
segmentation, i.e., classify each segment either as coming from concept “dog” (called concept segments), or not (called background segments).
• Pipeline– Perform unsupervised spatiotemporal segmentation.– Propose an algorithm to identify the meaningful segment.
Contributions
• Present a interpretation framework to analyze a broad class of existing weakly supervised learning algorithms about segment annotation problem.
• Propose a discriminative algorithm CRANE (Concept Ranking According to Negative Exemplars) for segment annotation.
Interpretation framework
• Pairwise distance matrix between segments
Segment: spatiotemporal volume (3D), represented as a point in feature space(such as RGB histogram, local binary pattern histogram, or dense optical histogram).
• Positive segment Concept segment Background segment
• Negative segment
Goal: classify the from in .
Rank the elements in in decreasing order of a score, such that top-rankedElements correspond to .
Interpretation framework• Baseline algorithms about segment annotation.– Kernel density estimation for Negative segments.
• Intuition: the distribution of is similar to distribution of .• Construct a probability density operated on block C.• Rank the elements according to .
– Negative Mining (MIN)• Intuition: distance from to the nearest > distance from to
nearest . • Operated on block D.
CRANE• Each negative segment in penalizes nearby segments in .• Segments in should be those far from negatives.
Penalty function