Saliency Detection Using DCT Coefficients and Superpixel-Based Segmentation

The salient region is the area of an image that attracts the attention of viewers. In this paper, a very effective saliency detection algorithm is proposed. Our algorithm is mainly based on two new techniques. First, the discrete cosine transform (DCT) is used for constructing the block-wise saliency map. Then, the superpixel-based segmentation is applied. Since DCT coefficients can reflect the color features of each block in the frequency domain and superpixels can well preserve object boundaries, with the two techniques, the performance of saliency detection can be significantly improved. The simulations performed on a database of 1000 images with human-marked ground truths show that our proposed method can extract the salient region very accurately and outperforms all of the existing saliency detection methods.

[1]  Vladimir Kolmogorov,et al.  An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision , 2004, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  S. Avidan,et al.  Seam carving for content-aware image resizing , 2007, SIGGRAPH 2007.

[3]  O. Sorkine,et al.  Optimized scale-and-stretch for image resizing , 2008, SIGGRAPH 2008.

[4]  Nanning Zheng,et al.  Proto-Object Based Rate Control for JPEG2000: An Approach to Content-Based Scalability , 2011, IEEE Transactions on Image Processing.

[5]  Edward Y. Chang,et al.  Parallel Spectral Clustering in Distributed Systems , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Shi-Min Hu,et al.  Sketch2Photo: internet image montage , 2009, ACM Trans. Graph..

[7]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[8]  Mubarak Shah,et al.  Visual attention detection in video sequences using spatiotemporal cues , 2006, MM '06.

[9]  Pietro Perona,et al.  Is bottom-up attention useful for object recognition? , 2004, CVPR 2004.

[10]  C. Koch,et al.  Computational modelling of visual attention , 2001, Nature Reviews Neuroscience.

[11]  S. Süsstrunk,et al.  Frequency-tuned salient region detection , 2009, CVPR 2009.

[12]  Nanning Zheng,et al.  Automatic salient object segmentation based on context and shape prior , 2011, BMVC.

[13]  HongJiang Zhang,et al.  Contrast-based image attention analysis by using fuzzy growing , 2003, MULTIMEDIA '03.

[14]  Sabine Süsstrunk,et al.  Salient Region Detection and Segmentation , 2008, ICVS.

[15]  Pietro Perona,et al.  Graph-Based Visual Saliency , 2006, NIPS.

[16]  Liqing Zhang,et al.  Saliency Detection: A Spectral Residual Approach , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  P. Heng,et al.  Resizing by symmetry-summarization , 2010, SIGGRAPH 2010.

[18]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Oscal T.-C. Chen,et al.  Automatically-Determined Region of Interest in JPEG 2000 , 2007, IEEE Transactions on Multimedia.

[20]  Yael Pritch,et al.  Saliency filters: Contrast based filtering for salient region detection , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Lihi Zelnik-Manor,et al.  Context-Aware Saliency Detection , 2012, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  King Ngi Ngan,et al.  Unsupervised extraction of visual attention objects in color images , 2006, IEEE Transactions on Circuits and Systems for Video Technology.

[23]  Shi-Min Hu,et al.  Global contrast based salient region detection , 2011, CVPR 2011.

[24]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.