演讲人: Carnegie Mellon University Dr. Yang Yi

讲座时间: 12月8日下午2点到3点半

讲座地点: 信息楼四楼学术报告厅

讲座内容:

Abstract:

Near-duplicate

Video Retrieval Near-duplicate video retrieval (NDVR) has recently attracted

lots of research attention due to the exponential growth of online videos. It

helps in many areas, such as copyright protection, video tagging, online video

usage monitoring, etc. Most of existing approaches use only a single feature to

represent a video for NDVR. However, a single feature is often insuf?cient to

characterize the video content. Besides, while the accuracy is the main concern

in previous literatures, the scalability of NDVR algorithms for large scale

video datasets has been rarely addressed. In this paper, we present a novel

approach - Multiple Feature Hashing (MFH) to tackle both the accuracy and the

scalability issues of NDVR. MFH preserves the local structure information of

each individual feature and also globally consider the local structures for all

the features to learn a group of hash functions which map the video keyframes

into the Hamming space and generate a series of binary codes to represent the

video dataset. We evaluate our approach on a public video dataset and a large

scale video dataset consisting of 132,647videos, which was collected from

YouTube by ourselves. The experiment results show that the proposed method

outperforms the state-of-the-art techniques in both accuracy and ef?ciency.

Bio:

Yi Yang received his Ph.D degree from Zhejiang

University, in Computer Science in 2010. He worked for the University of

Queensland as a postdoctoral research fellow from September 2010 to May 2011.

In May 2011, he joined the School of Computer Science at Carnegie Mellon

University, as a postroctoral research fellow. His research interests include

machine learning and its applications to multimedia content analysis and

computer vision, e.g. multimedia indexing and retrieval, image annotation,

video semantics understanding, etc.

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