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DeepFix: A Fully Convolutional Neural Network for Predicting Human Eye Fixations

Kruthiventi, Srinivas S S and Ayush, Kumar and Babu, Venkatesh R (2017) DeepFix: A Fully Convolutional Neural Network for Predicting Human Eye Fixations. In: IEEE TRANSACTIONS ON IMAGE PROCESSING, 26 (9). pp. 4446-4456.

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Official URL: http://dx.doi.org/10.1109/TIP.2017.2710620


Understanding and predicting the human visual attention mechanism is an active area of research in the fields of neuroscience and computer vision. In this paper, we propose DeepFix, a fully convolutional neural network, which models the bottom-up mechanism of visual attention via saliency prediction. Unlike classical works, which characterize the saliency map using various hand-crafted features, our model automatically learns features in a hierarchical fashion and predicts the saliency map in an end-to-end manner. DeepFix is designed to capture semantics at multiple scales while taking global context into account, by using network layers with very large receptive fields. Generally, fully convolutional nets are spatially invariant-this prevents them from modeling location-dependent patterns (e.g., centre-bias). Our network handles this by incorporating a novel location-biased convolutional layer. We evaluate our model on multiple challenging saliency data sets and show that it achieves the state-of-the-art results.

Item Type: Journal Article
Additional Information: Copy right for this article belongs to the IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
Department/Centre: Division of Interdisciplinary Sciences > Supercomputer Education & Research Centre
Date Deposited: 03 Aug 2017 10:08
Last Modified: 03 Aug 2017 10:08
URI: http://eprints.iisc.ac.in/id/eprint/57562

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