Monte Carlo Sampling based Salient Region Detection

In this paper, a simple and effective method is proposed for salient region detection. Based on the observation that salient regions tend to be compact, connected and surrounded, our original idea is to exploit these three kinds of prior knowledge. However, concepts of spatial structure (such as connectivity and surroundedness) only have definite meanings in binary images. Thus, a Monte Carlo Sampling based Saliency model is proposed. Our model has two main advantages over other methods. Firstly, the result of each sampling process is a binary map which can greatly simplify the combination with prior knowledge of spatial structure. Secondly, our method is naturally parallelized because every sampling process is independent with each other, which makes our method very efficient. Experimental results on two datasets show that, compared with eleven state-of-the-art methods, our approach has a competitive performance and also runs very fast.

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

[2]  S. Süsstrunk,et al.  SLIC Superpixels ? , 2010 .

[3]  Chaur-Heh Hsieh,et al.  Effective image retrieval techniques based on novel salient region segmentation and relevance feedback , 2010, Multimedia Tools and Applications.

[4]  Nanning Zheng,et al.  Learning to Detect a Salient Object , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Gerhard Stoll,et al.  A context-based approach to crop and scale video for broadcast applications , 2010, 2010 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB).

[6]  Christof Koch,et al.  Image Signature: Highlighting Sparse Salient Regions , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

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

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

[10]  Liang Lin,et al.  PISA: Pixelwise Image Saliency by Aggregating Complementary Appearance Contrast Measures with Spatial Priors , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  David Salesin,et al.  Gaze-based interaction for semi-automatic photo cropping , 2006, CHI.

[12]  Rainer Stiefelhagen,et al.  Quaternion-Based Spectral Saliency Detection for Eye Fixation Prediction , 2012, ECCV.

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

[14]  Sabine Süsstrunk,et al.  Frequency-tuned salient region detection , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  D. C. Beardslee,et al.  Readings in perception , 1958 .

[16]  Bernhard Schölkopf,et al.  A Nonparametric Approach to Bottom-Up Visual Saliency , 2006, NIPS.

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

[18]  Laurent Itti,et al.  An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

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

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

[21]  Hui Wang,et al.  A Target Tracking Technology Based on Visual Salient Features , 2012 .

[22]  David A. Clausi,et al.  Human Action Recognition Using Salient Opponent-Based Motion Features , 2010, 2010 Canadian Conference on Computer and Robot Vision.

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

[24]  S Ullman,et al.  Shifts in selective visual attention: towards the underlying neural circuitry. , 1985, Human neurobiology.

[25]  Yao-Hong Tsai.  Hierarchical Salient Point Selection for image retrieval , 2012, Pattern Recognit. Lett..

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

[27]  Touradj Ebrahimi,et al.  The JPEG2000 still image coding system: an overview , 2000, IEEE Trans. Consumer Electron..

[28]  Yuzhen Niu,et al.  Saliency Aggregation: A Data-Driven Approach , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

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

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

[31]  Benjamin W Tatler,et al.  The central fixation bias in scene viewing: selecting an optimal viewing position independently of motor biases and image feature distributions. , 2007, Journal of vision.