Salient object detection based on meanshift filtering and fusion of colour information

Colour and its spatial distribution are the main information currently used to detect salient objects in an image, but this cannot always guarantee satisfying performance. To deal with this problem, a salient object detection algorithm has been presented based on meanshift filtering and fusion of colour information. Superpixel segmentation is used to analyse the images by sets of pixels instead of single pixel, which improves the robustness of the algorithm to noises, as well as the efficiency. Meanshift filtering is used to detect the modes of every superpixel in spatial domain and range domain, respectively, which is the basis of the subsequent calculation. Each target therefore offers almost the same saliency and the spatial distribution of which will be easier to analyse. The fusion of colour contrast and colour concentration as well as centre prior is used as criterion to evaluate the saliency of every single superpixel. According to the tests of the algorithm on the open popular dataset, it has been proved that the algorithm presented in this work shows better results in both the aspect of effectiveness and efficiency, compared with its traditional equivalents.

[1]  Larry D. Hostetler,et al.  The estimation of the gradient of a density function, with applications in pattern recognition , 1975, IEEE Trans. Inf. Theory.

[2]  Rongrong Ji,et al.  RGBD Salient Object Detection: A Benchmark and Algorithms , 2014, ECCV.

[3]  Tiejun Huang,et al.  Visual Saliency with Statistical Priors , 2013, International Journal of Computer Vision.

[4]  Martin D. Levine,et al.  Visual Saliency Based on Scale-Space Analysis in the Frequency Domain , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Huchuan Lu,et al.  Graph-Regularized Saliency Detection With Convex-Hull-Based Center Prior , 2013, IEEE Signal Processing Letters.

[6]  Lihi Zelnik-Manor,et al.  What Makes a Patch Distinct? , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

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

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

[9]  Huchuan Lu,et al.  Saliency Detection via Absorbing Markov Chain , 2013, 2013 IEEE International Conference on Computer Vision.

[10]  Rama Chellappa,et al.  Entropy rate superpixel segmentation , 2011, CVPR 2011.

[11]  Huchuan Lu,et al.  Saliency Detection via Graph-Based Manifold Ranking , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Li Xu,et al.  Hierarchical Saliency Detection , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Zhi Liu,et al.  Efficient saliency detection based on gaussian models , 2011 .

[14]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

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

[16]  Huchuan Lu,et al.  Robust Superpixel Tracking , 2014, IEEE Transactions on Image Processing.

[17]  Zheru Chi,et al.  Salient object detection using content-sensitive hypergraph representation and partitioning , 2012, Pattern Recognit..

[18]  Ying Wu,et al.  A unified approach to salient object detection via low rank matrix recovery , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

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

[20]  Vladimir Kolmogorov,et al.  "GrabCut": interactive foreground extraction using iterated graph cuts , 2004, ACM Trans. Graph..

[21]  Yannis Avrithis,et al.  Bottom-up spatiotemporal visual attention model for video analysis , 2007 .