An Automatic Image Segmentation Algorithm Based on Three-Way Decisions

Image segmentation plays an important role in pattern recognition and computer vision. However, several problems not currently addressed affect the performance in segmenting objects from the background. First, color images tend to be divided into small patches, called over-segmentation. Second, most image segmentation algorithms are two-way decisions models, which will cause the inaccurate results when available information is insufficient. To address these problems, a three-way decisions based image segmentation algorithm is proposed in this study. Instead of dividing images into foreground and background as other methods have done, the proposed algorithm enables non-commitment for uncertain pixels and re-grows them using the information from neighboring regions. Due to the capability of reducing uncertainty and imprecision, three-way decisions is an effective method for image segmentation. The experimental results using PASCAL VOC 2007 demonstrate the effectiveness of the proposed algorithm. In addition, adopting the proposed algorithm in applications like CT image analysis results in a significant performance boost.

[1]  Yiyu Yao,et al.  The superiority of three-way decisions in probabilistic rough set models , 2011, Inf. Sci..

[2]  Haibo Zhang,et al.  A Three-Way Decision Clustering Approach for High Dimensional Data , 2016, IJCRS.

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

[4]  Huangjian Yi,et al.  Generalized three-way decision models based on subset evaluation , 2017, Int. J. Approx. Reason..

[5]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[6]  Zhifei Zhang,et al.  A three-way decisions model with probabilistic rough sets for stream computing , 2017, Int. J. Approx. Reason..

[7]  Huaxiong Li,et al.  Risk Decision Making Based on Decision-theoretic Rough Set: A Three-way View Decision Model , 2011, Int. J. Comput. Intell. Syst..

[8]  Jonathan Krause,et al.  Fine-grained recognition without part annotations , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Xiaodong Yue,et al.  Tri-partition neighborhood covering reduction for robust classification , 2017, Int. J. Approx. Reason..

[10]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Hans Burkhardt,et al.  A CONTENT-BASED IMAGE RETRIEVAL SCHEME IN JPEG COMPRESSED DOMAIN , 2006 .

[12]  B. Ginneken,et al.  Automatic segmentation of the lungs and lobes from thoracic CT scans , 2011 .

[13]  Min Chen,et al.  Qualitative and quantitative combinations of crisp and rough clustering schemes using dominance relations , 2014, Int. J. Approx. Reason..

[14]  M. Valliammai,et al.  Lungs Segmentation using Multi-level Thresholding in CT Images , 2012 .

[15]  A. V. Savchenko,et al.  Fast multi-class recognition of piecewise regular objects based on sequential three-way decisions and granular computing , 2016, Knowl. Based Syst..

[16]  Chao Lin,et al.  Dynamic Incorporation ofWavelet Filter in Fuzzy C-Means for Efficient and Noise-Insensitive MR Image Segmentation , 2015, Int. J. Comput. Intell. Syst..

[17]  Decui Liang,et al.  Incorporating logistic regression to decision-theoretic rough sets for classifications , 2014, Int. J. Approx. Reason..

[18]  Huimin Ma,et al.  Improving object proposals with multi-thresholding straddling expansion , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Shih-Fu Chang,et al.  Interactive Segmentation on RGBD Images via Cue Selection , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Yiyu Yao,et al.  An Outline of a Theory of Three-Way Decisions , 2012, RSCTC.

[21]  Zhihua Wei,et al.  A Self-adaptive Cascade ConvNets Model Based on Three-Way Decision Theory , 2017, CCCV.

[22]  Jingdong Wang,et al.  Salient Object Detection: A Discriminative Regional Feature Integration Approach , 2013, International Journal of Computer Vision.

[23]  Laurent Mouchard,et al.  Dealing with uncertainty and imprecision in image segmentation using belief function theory , 2014, Int. J. Approx. Reason..

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

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

[26]  Pradipta Maji,et al.  Spatially Constrained Student’s t-Distribution Based Mixture Model for Robust Image Segmentation , 2018, Journal of Mathematical Imaging and Vision.

[27]  Yiyu Yao,et al.  Three-way decisions with probabilistic rough sets , 2010, Inf. Sci..

[28]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Charless C. Fowlkes,et al.  Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Yiyu Yao,et al.  Three-Way Decision: An Interpretation of Rules in Rough Set Theory , 2009, RSKT.

[31]  Zhihua Wei,et al.  Optimized Automatic Seeded Region Growing Algorithm with Application to ROI Extraction , 2017, Int. J. Image Graph..

[32]  Bing Huang,et al.  Cost-sensitive sequential three-way decision modeling using a deep neural network , 2017, Int. J. Approx. Reason..

[33]  智一 吉田,et al.  Efficient Graph-Based Image Segmentationを用いた圃場図自動作成手法の検討 , 2014 .

[34]  Matti Pietikäinen,et al.  Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2000, ECCV.

[35]  Zhifei Zhang,et al.  Determining Thresholds in Three-Way Decisions: A Multi-object Optimization View , 2017, IJCRS.

[36]  B. S. Manjunath,et al.  Unsupervised Segmentation of Color-Texture Regions in Images and Video , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[37]  Kai Xiao,et al.  Dynamic Incorporation of Wavelet Filter in Fuzzy C-Means for Efficient and Noise-Insensitive MR Image Segmentation , 2015 .

[38]  Yiyu Yao,et al.  Rough Sets and Three-Way Decisions , 2015, RSKT.

[39]  Feiping Nie,et al.  Heterogeneous image feature integration via multi-modal spectral clustering , 2011, CVPR 2011.

[40]  Lei Wang,et al.  Chinese Emotion Recognition Based on Three-Way Decisions , 2015, RSKT.

[41]  Md. Monirul Islam,et al.  A review on automatic image annotation techniques , 2012, Pattern Recognit..

[42]  Yiyu Yao,et al.  Granular Computing and Sequential Three-Way Decisions , 2013, RSKT.

[43]  Xinjian Chen,et al.  Random Walk and Graph Cut for Co-Segmentation of Lung Tumor on PET-CT Images , 2015, IEEE Transactions on Image Processing.