Graduate School and Research Center in Digital Sciences

Improving video concept detection through label space partitioning

Niaz, Usman; Merialdo, Bernard

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.

Document Doi Bibtex

Title:Improving video concept detection through label space partitioning
Keywords:Error correcting codes, multi-label classification, video concept detection
Department:Data Science
Eurecom ref:4295
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Bibtex: @inproceedings{EURECOM+4295, doi = {}, year = {2014}, title = {{I}mproving video concept detection through label space partitioning}, author = {{N}iaz, {U}sman and {M}erialdo, {B}ernard }, booktitle = {{ICME} 2014, {IEEE} {I}nternational {C}onference on {M}ultimedia and {E}xpo, 14-18 {J}uly 2014, {C}hengdu, {C}hina}, address = {{C}hengdu, {CHINA}}, month = {07}, url = {} }
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