FUNNRAR: Hybrid rarity/learning visual saliency
| Autor: | S. Duzelier, Nicolas Riche, Bernard Gosselin, Marc Decombas, Robert Laganiere, Pierre Marighetto, Jérémie Jakubowicz, Matei Mancas, I. Hadj Abdelkader |
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| Rok vydání: | 2016 |
| Předmět: |
Artificial neural network
Pixel business.industry Computer science Feature extraction Contrast (statistics) 020206 networking & telecommunications Pattern recognition 02 engineering and technology Visualization Feature (computer vision) Salience (neuroscience) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Saliency map Computer vision Artificial intelligence business Fusion mechanism |
| Zdroj: | ICIP |
| DOI: | 10.1109/icip.2016.7532866 |
| Popis: | Saliency models provide heatmaps highlighting the probability of each pixel to attract human gaze. To define image's important regions, features maps are extracted. The rarity, surprise or contrast are computed leading to conspicuity maps, showing important regions of each feature map. The final saliency map is obtained by merging these maps. The fusion process is usually a linear combination of the maps where the coefficients show their importance. We propose a novel generic fusion mechanism based on 1) using a rarity-based attention module and 2) using neural networks to achieve the fusion. The first layer of the NN merges the weighted feature maps into a saliency map. The second layer takes into account the spatial information. The approach is compared to 8 models using 4 different comparison metrics on open state-of-the-art databases. |
| Databáze: | OpenAIRE |
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