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Content Provider | ACM Digital Library |
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Author | Wu, Shuang Zhuang, Xiaodan Natarajan, Pradeep |
Abstract | Bag-of-words approaches have been shown to achieve state-of-the-art performance in large-scale multimedia event detection. However, the commonly used histogram representation of bag-of-words requires large codebook sizes and expensive nonlinear kernel based classifiers for optimal performance. To address these two issues, we present a two-part generative model for compact visual representation, based on the i-vector approach recently proposed for speech and audio modeling. First, we use a Gaussian mixture model (GMM) to model the joint distribution of local descriptors. Second, we use a low-dimensional factor representation that constrains the GMM parameters to a subspace that preserves most of the information. We further extend this method to incorporate overlapping spatial regions, forming a highly compact visual representation that achieves superior performance with fast linear classifiers. We evaluate the method on a large video dataset used in the TRECVID 2011 MED evaluation. With linear classifiers, the proposed representation, with one-tenth of the storage footprint, outperforms soft quantization histograms used in the top performing TRECVID 2011 MED systems. |
Starting Page | 521 |
Ending Page | 524 |
Page Count | 4 |
File Format | |
ISBN | 9781450324045 |
DOI | 10.1145/2502081.2502138 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2013-10-21 |
Publisher Place | New York |
Access Restriction | Subscribed |
Subject Keyword | Bag of words Linear classifier Generative model |
Content Type | Text |
Resource Type | Article |
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