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Mining Videos for Features that Drive Attention

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Technical Report

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University of Southern California Los Angeles United States

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Certain features of a video capture human attention and this can be measured by recording eye movements of a viewer. Using this technique combined with extraction of various types of features from video frames, one can begin to understand what features of a video may drive attention. In this chapter we define and assess different types of feature channels that can be computed from video frames, and compare the output of these channels to human eye movements. This provides us with a measure of how well a particular feature of a video can drive attention. We then examine several types of channel combinations and learn a set of weightings of features that can best explain human eye movements. A linear combination of features with high weighting on motion and color channels was most predictive of eye movements on a public dataset.

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