Video saliency detection based on spatiotemporal feature learning

Se Ho Lee, Jin Hwan Kim, Kwang Pyo Choi, Jae Young Sim, Chang-Su Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Citations (Scopus)


A video saliency detection algorithm based on feature learning, called ROCT, is proposed in this work. To detect salient regions, we design multiple spatiotemporal features and combine those features using a support vector machine (SVM). We extract the spatial features of rarity, compactness, and center prior by analyzing the color distribution in each image frame. Also, we obtain the temporal features of motion intensity and motion contrast to identify visually important motions. We train an SVM classifier using the spatiotemporal features extracted from training video sequences. Finally, we compute the visual saliency of each patch in an input sequence using the trained classifier. Experimental results demonstrate that the proposed algorithm provides more accurate and reliable results of saliency detection than conventional algorithms.

Original languageEnglish
Title of host publication2014 IEEE International Conference on Image Processing, ICIP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Print)9781479957514
Publication statusPublished - 2014 Jan 28


  • machine learning
  • spatiotemporal features
  • support vector machine
  • Video saliency detection

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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