A Novel Video Classification Method Based on Hybrid Generative/Discriminative Models

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• 作者：Zhi Zeng ; Wei Liang ; Heping Li ; Shuwu Zhang
• 关键词：Video classification ; pLSA ; audio content mining
• 刊名：Lecture Notes in Computer Science
• 出版年：2008
• 出版时间：2008
• 年：2008
• 卷：5342
• 期：1
• 页码：705-713
• 全文大小：631.4 KB
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

We consider the problem of automatically classifying videos into predefined categories based on the analysis of their audio contents. In detail, given a set of labeled videos (such as news, sitcoms, sports, etc.), our objective is to classify a new video into one of these categories. To solve this problem, a novel audio features based video classification method combining an unsupervised generative model named probabilistic Latent Semantic Analysis (pLSA) with a multi-class discriminative classifier is proposed. Since general audio signals usually show complicated distribution in the feature space, k-means clustering method is firstly used to group temporal signal segments with similar low-level features into natural clusters, which are adopted as “audio words”. Then, the audio stream of a video is decomposed into a bag of “audio words”. To classify those bags of “audio words” which extracted from videos, latent “topics” are discovered by pLSA, and subsequently, training a multi-class classifier on the “topic” distribution vector for each video. Encouraging classification results have been achieved in our experiments.