Saliency aggregation: Does unity make strength?

Olivier Le Meur 1, * Zhi Liu 2
* Corresponding author
1 Sirocco - Analysis representation, compression and communication of visual data
Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : In this study, we investigate whether the aggregation of saliency maps allows to outperform the best saliency models. This paper discusses various aggregation methods; six unsupervised and four supervised learning methods are tested on two existing eye fixation datasets. Results show that a simple average of the TOP 2 saliency maps significantly outperforms the best saliency models. Considering more saliency models tends to decrease the performance, even when robust aggregation methods are used. Concerning the supervised learning methods, we provide evidence that it is possible to further increase the performance, under the condition that an image similar to the input image can be found in the training dataset. Our results might have an impact for critical applications which require robust and relevant saliency maps.
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Olivier Le Meur, Zhi Liu. Saliency aggregation: Does unity make strength?. 12th Asian Conference on Computer Vision (ACCV 2014), Nov 2014, Singapor, Singapore. ⟨hal-01085898⟩

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