Instead of occurring independently, semantic concepts pairs tend to co-occur within a single image and it is intuitive that concept detection accuracy for visual concepts can be enhanced if concept correlation can be leveraged in some way. In everyday concept detection for visual lifelogging using wearable cameras to automatically record every- day activities, the captured images usually have a diversity of concepts which challenges the performance of concept detection. In this paper a semantically smoothed refinement algorithm is proposed using concept correlations which exploit topic-related concept relationships, modeled externally in a user experiment rather than extracted from training data. Results for initial concept detection are factorized based on semantic smoothness and adjusted in compliance with the extracted concept correlations. Refinement performance is demonstrated in experiments to show the effectiveness of our algorithm and the extracted correlations.
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Funders:
Science Foundation Ireland SFI/12/RC/2289., National Natural Science Foundation of China Grant No. 2011CB302206, National Natural Science Foundation of China Grant No. 61272231, 61472204, 61502264
ID Code:
21508
Deposited On:
08 Dec 2016 16:08 by
Alan Smeaton
. Last Modified 31 Oct 2018 11:36