Video Highlights and Video Statistics in First Place

Video Highlights and Video Statistics in First Place – Automatically identifying keypoints is a challenging problem in data mining. Recent work has shown that finding the most important keypoints is a multi-step problem, which has led to many successful research efforts. However, keypoints are commonly not detected automatically after an important step in a system architecture. This paper presents an algorithm for identifying keypoints with high probability. The algorithm is an extension of the traditional one-step learning algorithms. It aims to find keypoints that lie in a global proximity in data. We present an algorithm for the identification of keypoints with high probability. We first show how to identify keypoints that lie in a global proximity using a sparse metric, and then propose a variant of the algorithm that can be used to find keypoint locations. The algorithm is evaluated on the problem of finding the most important keypoints by means of a simple and fast algorithm. Experimental results show that our algorithm outperforms other related keypoint mining algorithms.

We present a method for learning two-dimensional conditional probability distribution functions on a continuous set of frames. This is in contrast to several existing approaches, including the recently proposed approach for conditional probability estimation in the sense of conditional random field. The aim of the new approach is to learn a conditional probability distribution on the observed frames in order to obtain a more efficient conditional estimator. We illustrate that this approach improves the performance of conditional regression in a simulated application, where we show that the number of frames on test set are much smaller than those on typical real world images.

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Video Highlights and Video Statistics in First Place

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  • A Unified Approach to Multi-Person Identification and Movement Identification using Partially-Occurrence Multilayer Networks

    Video Frame Interpolation via Joint Determinantal and Dose CodingWe present a method for learning two-dimensional conditional probability distribution functions on a continuous set of frames. This is in contrast to several existing approaches, including the recently proposed approach for conditional probability estimation in the sense of conditional random field. The aim of the new approach is to learn a conditional probability distribution on the observed frames in order to obtain a more efficient conditional estimator. We illustrate that this approach improves the performance of conditional regression in a simulated application, where we show that the number of frames on test set are much smaller than those on typical real world images.


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