This paper presents a novel deep architecture for saliency prediction. Current state of the art models for saliency prediction employ Fully Convolutional networks that perform a non-linear combination of features extracted from the last convolutional layer to predict saliency maps. We propose an architecture which, instead, combines features extracted at different levels of a Convolutional Neural Network (CNN). Our model is composed of three main blocks: a feature extraction CNN, a feature encoding network, that weights low and high level feature maps, and a prior learning network. We compare our solution with state of the art saliency models on two public benchmarks datasets. Results show that our model outperforms under all evaluation metrics on the SALICON dataset, which is currently the largest public dataset for saliency prediction, and achieves competitive results on the MIT300 benchmark.

A Deep Multi-Level Network for Saliency Prediction / Cornia, Marcella; Baraldi, Lorenzo; Serra, Giuseppe; Cucchiara, Rita. - (2016). ((Intervento presentato al convegno 23rd International Conference on Pattern Recognition tenutosi a Cancun, Mexico nel 4-8 Dec 2016 [10.1109/ICPR.2016.7900174].

A Deep Multi-Level Network for Saliency Prediction

CORNIA, MARCELLA;BARALDI, LORENZO;SERRA, GIUSEPPE;CUCCHIARA, Rita
2016

Abstract

This paper presents a novel deep architecture for saliency prediction. Current state of the art models for saliency prediction employ Fully Convolutional networks that perform a non-linear combination of features extracted from the last convolutional layer to predict saliency maps. We propose an architecture which, instead, combines features extracted at different levels of a Convolutional Neural Network (CNN). Our model is composed of three main blocks: a feature extraction CNN, a feature encoding network, that weights low and high level feature maps, and a prior learning network. We compare our solution with state of the art saliency models on two public benchmarks datasets. Results show that our model outperforms under all evaluation metrics on the SALICON dataset, which is currently the largest public dataset for saliency prediction, and achieves competitive results on the MIT300 benchmark.
23rd International Conference on Pattern Recognition
Cancun, Mexico
4-8 Dec 2016
Cornia, Marcella; Baraldi, Lorenzo; Serra, Giuseppe; Cucchiara, Rita
A Deep Multi-Level Network for Saliency Prediction / Cornia, Marcella; Baraldi, Lorenzo; Serra, Giuseppe; Cucchiara, Rita. - (2016). ((Intervento presentato al convegno 23rd International Conference on Pattern Recognition tenutosi a Cancun, Mexico nel 4-8 Dec 2016 [10.1109/ICPR.2016.7900174].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1103794
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