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The most popular approach to large scale image retrieval is based on the bag-of-visual-word (BoV) representation of images. The spatial information is usually reintroduced as a post-processing step to re-rank the retrieved images, through a spatial verification like RANSAC. Since the spatial verification techniques are computationally expensive , they can(More)
The sliding window approach has been widely used for object detection because it provides a simple way to apply object recognition techniques to the detection task. Despite its effectiveness, though, the exhaustive search makes the approach inefficient in the case of a non-trivial classifier. The branch and bound techniques [3, 4] have been proposed in(More)
The local feature based approaches have become popular for activity recognition. A local feature captures the local movement and appearance of a local region in a video, and thus can be ambiguous; e.g., it cannot tell whether a movement is from a person's hand or foot, when the camera is far away from the person. To better distinguish different types of(More)
High order features have been proposed to incorporate geometrical information into the "bag of feature" representation. We propose algorithms to perform fast weakly supervised object categorization and localization with high order features. To this end, we first use Hough transform method to identify translation and scale invariant high order features(More)
Bayesian theory has provided a compelling conceptualization for perceptual inference in the brain. Central to Bayesian inference is the notion of statistical priors. To understand the neural mechanisms of Bayesian inference, we need to understand the neural representation of statistical regularities in the natural environment. In this paper, we investigated(More)
We address the problem of group-level event recognition from videos. The events of interest are defined based on the motion and interaction of members in a group over time. Example events include group formation, dispersion, following , chasing, flanking, and fighting. To recognize these complex group events, we propose a novel approach that learns the(More)