A Comparative Study of Interactive Segmentation with Different Number of Strokes on Complex Images

Kok Luong Goh, Giap Weng Ng, Muzaffar Hamzah, Soo See Chai

Abstract


Interactive image segmentation is the way to extract an object of interest with the guidance of the user. The guidance from the user is an iterative process until the required object of interest had been segmented. Therefore, the input from the user as well as the understanding of the algorithms based on the user input has an essential role in the success of interactive segmentation. The most common user input type in interactive segmentation is using strokes. The different number of strokes are utilized in each different interactive segmentation algorithms. There was no evaluation of the effects on the number of strokes on this interactive segmentation. Therefore, this paper intends to fill this shortcoming. In this study, the input strokes had been categorized into single, double, and multiple strokes. The use of the same number of strokes on the object of interest and background on three interactive segmentation algorithms: i) Nonparametric Higher-order Learning (NHL), ii) Maximal Similarity-based Region Merging (MSRM) and iii) Graph-Based Manifold Ranking (GBMR) are evaluated, focusing on the complex images from Berkeley image dataset. This dataset contains a total of 12,000 test color images and ground truth images. Two types of complex images had been selected for the experiment: image with a background color like the object of interest, and image with the object of interest overlapped with other similar objects.   This can be concluded that, generally, more strokes used as input could improve image segmentation accuracy.


Keywords


image segmentation; interactive segmentation; user input; strokes; complex image.

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References


Malmberg, F., et al., SmartPaint: a tool for interactive segmentation of medical volume images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2017. 5(1): p. 36-44.

Ozdemir, F., et al., Interactive segmentation in MRI for orthopedic surgery planning: bone tissue. International Journal of Computer Assisted Radiology and Surgery, 2017. 12(6): p. 1031-1039.

Vasuki, Y., et al., An interactive image segmentation method for lithological boundary detection: A rapid mapping tool for geologists. Computers & Geosciences, 2017. 100: p. 27-40.

Ma, J., et al., A segmentation method for greenhouse vegetable foliar disease spots images using color information and region growing. Computers and Electronics in Agriculture, 2017. 142: p. 110-117.

Zhou, C., et al., An efficient two-stage region merging method for interactive image segmentation. Computers & Electrical Engineering, 2016. 54: p. 220-229.

Madani, E.M. and M. Torki. Interactive Image Segmentation Using Multimodal Regularized Kernel Embedding. in 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). 2018.

Jian, M. and C. Jung, Interactive Image Segmentation Using Adaptive Constraint Propagation. IEEE Transactions on Image Processing, 2016. 25(3): p. 1301-1311.

Park, S., H.S. Lee, and J. Kim. Seed growing for interactive image segmentation with geodesic voting. in 2016 IEEE International Conference on Image Processing (ICIP). 2016.

Yu, H., et al. Loosecut: Interactive image segmentation with loosely bounded boxes. in 2017 IEEE International Conference on Image Processing (ICIP). 2017.

He, K., et al., Interactive Image Segmentation on Multiscale Appearances. IEEE Access, 2018. 6: p. 67732-67741.

Li, X., K. Liu, and Y. Dong, Superpixel-Based Foreground Extraction with Fast Adaptive Trimaps. IEEE Transactions on Cybernetics, 2018. 48(9): p. 2609-2619.

Feng, J., et al. Interactive Segmentation on RGBD Images via Cue Selection. in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016.

Li, Z., Q. Chen, and V. Koltun. Interactive Image Segmentation with Latent Diversity. in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2018.

Zadicario, E., et al. Boundary snapping for robust image cutouts. in 2008 IEEE Conference on Computer Vision and Pattern Recognition. 2008.

de Miranda, P.A.V., A.X. Falcão, and J.K. Udupa, Synergistic arc-weight estimation for interactive image segmentation using graphs. Computer Vision and Image Understanding, 2010. 114(1): p. 85-99.

Luo, L., et al., Interactive image segmentation based on samples reconstruction and FLDA. Journal of Visual Communication and Image Representation, 2017. 43: p. 138-151.

Kim, T.H., K.M. Lee, and S.U. Lee. Nonparametric higher-order learning for interactive segmentation. in 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2010.

Ning, J., et al., Interactive image segmentation by maximal similarity-based region merging. Pattern Recogn., 2010. 43(2): p. 445-456.

Li, H., W. Wu, and E. Wu, Robust interactive image segmentation via graph-based manifold ranking. Computational Visual Media, 2015. 1(3): p. 183-195.

Martin, D., et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. in Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001. 2001.

Sharma, M.a.V.C., Objective evaluation parameters of image segmentation algorithms. International Journal of Engineering and Advanced Technology (IJEAT), 2012. 2(2): p. 2249-8958.




DOI: http://dx.doi.org/10.18517/ijaseit.10.1.10240

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