Salient object extraction in low depth-of-field images using SVDD

Existing salient object extraction methods for the low depth-of-field (DOF) image are usually based on local saliency. However, in the low DOF image, the smooth region of salient objects is similar to the background in local saliency, so they are easily confused. In this paper, a novel salient object extraction method is proposed by introducing Support Vector Data Description (SVDD) for salient object shape description. It is the first time that SVDD is used for salient object extraction. SVDD makes full use of global characteristics of salient objects, which makes it possible for our approach to accurately extract salient objects containing smooth regions. Experiments on a Flickr dataset consisting of 141 low DOF images indicate that F-measure of our approach is better than the existing methods.

[1]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[2]  Robert P. W. Duin,et al.  Support Vector Data Description , 2004, Machine Learning.

[3]  King Ngi Ngan,et al.  Unsupervized Video Segmentation With Low Depth of Field , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

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

[5]  Ariel Shamir,et al.  Seam Carving for Content-Aware Image Resizing , 2007, ACM Trans. Graph..

[7]  Huijun Gao,et al.  A Curve Evolution Approach for Unsupervised Segmentation of Images With Low Depth of Field , 2013, IEEE Transactions on Image Processing.

[8]  Wai Lok Woo,et al.  Automatic Segmentation of Interest Regions in Low Depth of Field Images Using Ensemble Clustering and Graph Cut Optimization Approaches , 2012, 2012 IEEE International Symposium on Multimedia.

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

[10]  Zheru Chi,et al.  Attention-driven image interpretation with application to image retrieval , 2006, Pattern Recognit..

[11]  Fei-Fei Li,et al.  Deep visual-semantic alignments for generating image descriptions , 2015, CVPR.

[12]  Zhi Liu,et al.  Automatic segmentation of focused objects from images with low depth of field , 2010, Pattern Recognit. Lett..

[13]  Vladlen Koltun,et al.  Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials , 2011, NIPS.

[14]  Wai Lok Woo,et al.  Region-of-interest extraction in low depth of field images using ensemble clustering and difference of Gaussian approaches , 2013, Pattern Recognit..

[15]  Peng Jiang,et al.  Salient Region Detection by UFO: Uniqueness, Focusness and Objectness , 2013, 2013 IEEE International Conference on Computer Vision.

[16]  Changick Kim,et al.  Segmenting a low-depth-of-field image using morphological filters and region merging , 2005, IEEE Transactions on Image Processing.

[17]  Iasonas Kokkinos,et al.  Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs , 2014, ICLR.

[18]  Chi-Keung Tang,et al.  KNN Matting , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Hans-Peter Kriegel,et al.  Robust segmentation of relevant regions in low depth of field images , 2011, 2011 18th IEEE International Conference on Image Processing.

[20]  Du-Ming Tsai,et al.  Segmenting focused objects in complex visual images , 1998, Pattern Recognit. Lett..

[21]  Zhuowen Tu,et al.  Deeply Supervised Salient Object Detection with Short Connections , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  James Ze Wang,et al.  Unsupervised Multiresolution Segmentation for Images with Low Depth of Field , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Hans-Peter Kriegel,et al.  Robust Image Segmentation in Low Depth Of Field Images , 2013, ArXiv.

[24]  Zhen Ye,et al.  Unsupervised Multiscale Focused Objects Detection Using Hidden Markov Tree , 2002, JCIS.