VIDEO SALIENCY DETECTION BASED ON SPATIOTEMPORAL FEATURE LEARNING

被引:0
作者
Lee, Se-Ho [1 ]
Kim, Jin-Hwan [1 ]
Choi, Kwang Pyo [2 ]
Sim, Jae-Young [3 ]
Kim, Chang-Su [1 ]
机构
[1] Korea Univ, Sch Elect Engn, Seoul, South Korea
[2] Samsung Elect, Digital Media & Commun R&D Ctr, Suwon, South Korea
[3] Ulsan Natl Inst Sci & Technol, Sch Elect & Comp Engn, Ulsan, South Korea
来源
2014 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2014年
关键词
Video saliency detection; spatiotemporal features; support vector machine; machine learning; VISUAL-ATTENTION; IMAGE; EXTRACTION; OBJECTS; MODEL;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
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.
引用
收藏
页码:1120 / 1124
页数:5
相关论文
共 20 条
[1]  
Achanta R, 2009, PROC CVPR IEEE, P1597, DOI 10.1109/CVPRW.2009.5206596
[2]   Fully Automatic Extraction of Salient Objects from Videos in Near Real Time [J].
Akamine, Kazuma ;
Fukuchi, Ken ;
Kimura, Akisato ;
Takagi, Shigeru .
COMPUTER JOURNAL, 2012, 55 (01) :3-14
[3]  
Borji A., 2012, CVPR, DOI DOI 10.1109/CVPR.2012.6247706
[4]   LIBSVM: A Library for Support Vector Machines [J].
Chang, Chih-Chung ;
Lin, Chih-Jen .
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2011, 2 (03)
[5]   A Novel Multiresolution Spatiotemporal Saliency Detection Model and Its Applications in Image and Video Compression [J].
Guo, Chenlei ;
Zhang, Liming .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2010, 19 (01) :185-198
[6]   Unsupervised extraction of visual attention objects in color images [J].
Han, JW ;
Ngan, KN ;
Li, MJ ;
Zhang, HH .
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2006, 16 (01) :141-145
[7]  
Harel J., 2006, Graph-Based Visual Saliency, V19, DOI DOI 10.7551/MITPRESS/7503.003.0073
[8]   A model of saliency-based visual attention for rapid scene analysis [J].
Itti, L ;
Koch, C ;
Niebur, E .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1998, 20 (11) :1254-1259
[9]  
Judd T, 2009, IEEE I CONF COMP VIS, P2106, DOI 10.1109/ICCV.2009.5459462
[10]  
Kim JS, 2009, PROC CVPR IEEE, P1730, DOI 10.1109/CVPRW.2009.5206666