Improved saliency detection based on Bayesian framework for object proposal

In this paper, a new method is proposed for object proposal based on saliency detection. First, a novel method is proposed to measure the global spatial compact distribution of the color components in an image. The saliency detection method proposed on the basis of Bayesian improves the estimation of prior probability and likelihood of observations by means of an optimized boundary connectivity measure. Second, based on the saliency map of the method proposed, the object proposal is given with the bounding box, through non-maxima suppression sampling strategy. Both, the saliency detection method and the object proposal method, are evaluated and compared with state-of-the-art results on standard databases. The experimental results on the challenging PASCAL VOC2007 data set show that the detection rate of the object proposal method proposed can reach 93.4% for the first 1000 windows proposed.

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

[2]  Xiaoyun Zhang,et al.  Saliency detection using boundary information , 2014, Multimedia Systems.

[3]  Deepu Rajan,et al.  Salient Region Detection by Modeling Distributions of Color and Orientation , 2009, IEEE Transactions on Multimedia.

[4]  Vibhav Vineet,et al.  Efficient Salient Region Detection with Soft Image Abstraction , 2013, 2013 IEEE International Conference on Computer Vision.

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

[6]  Philip H. S. Torr,et al.  BING: Binarized normed gradients for objectness estimation at 300fps , 2019, Computational Visual Media.

[7]  Jonathan Warrell,et al.  Proposal generation for object detection using cascaded ranking SVMs , 2011, CVPR 2011.

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

[9]  Jian Sun,et al.  Geodesic Saliency Using Background Priors , 2012, ECCV.

[10]  Zhixun Su,et al.  Saliency detection based on an edge-preserving filter , 2013, 2013 IEEE International Conference on Image Processing.

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

[12]  Jianguo Zhang,et al.  The PASCAL Visual Object Classes Challenge , 2006 .

[13]  Luc Van Gool,et al.  Efficient Non-Maximum Suppression , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[14]  Huchuan Lu,et al.  Bayesian Saliency via Low and mid Level Cues , 2022 .

[15]  Esa Rahtu,et al.  Segmenting Salient Objects from Images and Videos , 2010, ECCV.

[16]  Jian Sun,et al.  Saliency Optimization from Robust Background Detection , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Thomas Deselaers,et al.  Measuring the Objectness of Image Windows , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[19]  Qi Tian,et al.  Saliency Density Maximization for Efficient Visual Objects Discovery , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[20]  Marie-Pierre Jolly,et al.  Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images , 2001, ICCV.

[21]  Huchuan Lu,et al.  Visual saliency detection based on Bayesian model , 2011, 2011 18th IEEE International Conference on Image Processing.

[22]  Koen E. A. van de Sande,et al.  Selective Search for Object Recognition , 2013, International Journal of Computer Vision.