Stochastic Sub-sampling of Environmental Saliency

Compressed-Sampling-Based Image Saliency Detection in the Wavelet Domain

When watching natural scenes, an overwhelming amount of information is delivered to the Human Visual System (HVS). The optic nerve is estimated to receive around 108 bits of information a second. This large amount of information can’t be processed right away through our neural system. Visual attention mechanism enables HVS to spend neural resources efficiently, only on the selected parts of the...

Action recognition by saliency-based dense sampling

Action recognition, aiming to automatically classify actions from a series of observations, has attracted more attention in the computer vision community. The state-of-the-art action recognition methods utilize dense sampled trajectories to build feature representations. However, their performances are limited due to action region clutters and camera motions in real world applications. No matte...

Sampling-based Sub-Trajectory Clustering

Sampling and sampling strategies for environmental analysis

Sampling errors are generally believed to dominate the errors of analytical measurement during the entire environmental data acquisition process. Unfortunately, environmental sampling errors are hardly quantified and documented even though analytical errors are frequently yet improperly reported to the third decimal point in environmental analysis. There is a significant discrepancy in directly...

A Visual Saliency Map Based on Random Sub-window Means

In this article, we propose a simple and efficient method for computing an image saliency map, which performs well on both salient region detection and as well as eye gaze prediction tasks. A large number of distinct sub-windows with random co-ordinates and scales are generated over an image. The saliency descriptor of a pixel within a random sub-window is given by the absolute difference of it...