A novel specific image scenes detection method

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• 作者：Yuxiang Xie (1)
  Xiao-Ping Zhang (2)
  Xidao Luan (3)
  Li Liu (1)
  Xin Zhang (1)
  
  1. Science and Technology on Information System Engineering Laboratory ; National University of Defense Technology ; Changsha ; 410073 ; People鈥檚 Republic of China
  2. Department of Electrical and Computer Engineering ; Ryerson University ; Toronto ; Canada
  3. Changsha University ; Changsha ; 410003 ; People鈥檚 Republic of China
  
• 关键词：Scene detection ; Feature extraction ; Local invariant feature ; SIFT
• 刊名：Multimedia Tools and Applications
• 出版年：2015
• 出版时间：January 2015
• 年：2015
• 卷：74
• 期：1
• 页码：105-122
• 全文大小：1,465 KB
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• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems
  Computer Communication Networks
  Data Structures, Cryptology and Information Theory
  Special Purpose and Application-Based Systems
  
• 出版者：Springer Netherlands
• ISSN：1573-7721

文摘

Automatic image scene detection is a crucial step for various tasks in computer vision. Current scene detection methods are often computationally expensive for use in real-time image classification. In this paper, a novel and efficient scene detection method based on local invariant features is presented. First, the SIFT feature detector and descriptor has been utilized to extract local image features since the SIFT descriptor has been proved to be an excellent local method that yields high quality features. However, the SIFT descriptor has been shown to produce high dimensional and redundant local features, which can create processing difficulty and computational burden in the successive classification stage. Therefore, two new feature selection strategies are proposed to reduce the number of SIFT keypoints and hence reduce the computational complexity. In both strategies, each image is represented by a single feature vector which assures the efficiency. Finally, a multi-classifier based on a support vector machine is applied to perform the scene detection task. Experimental results show that the proposed method can achieve accurate satisfactory classification results with significantly reduced computational complexity.