Learning visual saliency using topographic independent component analysis

Understanding the underlying mechanisms that drive human visual attention is a topic of immense interest. Most of the work is focused on extracting manually selected features that might resemble the human visual processing pathway and using a combination of those features to train a classifier that learns to predict where humans look. In contrast, we will learn the features using a generalization of Independent Component Analysis (ICA), namely the topographic Independent Component Analysis (tICA). We will show that those learned features in combination with linear SVM outperform the hand-crafted ones. In addition, we propose a novel optimization scheme, which jointly optimizes for linear SVM and tICA pooling weights and show that it further improves the results.