ICME 2014, IEEE International Conference on Multimedia and Expo, 14-18 July 2014, Chengdu, China
We present an approach to video concept detection by building binary trees partitioning the label space, using visual and semantic similarity for multi-label datasets. The technique overcomes sparse annotations problem by increasing the number of positive examples per concept with the number of classifiers per concept, though sub-optimal, augmented too. We draw similarities between the proposed tree generation approach and Error Correcting Output Codes (ECOC) for multi-label classification and build ranked lists of video shots using weighted decoding or weighted tree traversal. We build a set of different trees based on the presented criterion each
partitioning the label space in its own specific way. Inspired by the work in [1] we amass information from ensemble of trees to build the final ranked list, but using a different criterion.
The classification resulting in ensemble error correction is complementary to One-vs-All classification and increases concept detection performance significantly on the TRECVID
2010 and 2013 datasets.
Type:
Conférence
City:
Chengdu
Date:
2014-07-14
Department:
Data Science
Eurecom Ref:
4295
Copyright:
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