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.

In this paper, we present a new method that automatically selects face images for each user based on prior knowledge. Specifically, we use Google Now, an image search service, to detect facial features of users from their videos and image sequences. We have developed a deep neural network that automatically learns how to extract such features using a deep learning framework from images. To this end, we evaluate the new technique by a dataset of human face images under human face datasets, demonstrating that it significantly outperformed the human model.

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Video Highlights and Video Statistics in First Place

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  • On the Inclusion of Local Signals in Nonlinear Models

    Evaluation of Feature-based Face Recognition Methods: A Preliminary ReportIn this paper, we present a new method that automatically selects face images for each user based on prior knowledge. Specifically, we use Google Now, an image search service, to detect facial features of users from their videos and image sequences. We have developed a deep neural network that automatically learns how to extract such features using a deep learning framework from images. To this end, we evaluate the new technique by a dataset of human face images under human face datasets, demonstrating that it significantly outperformed the human model.


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