Video object extraction using optimized smoothed dirichlet process multi-view learning with improved adaptive modified Markova random field

  • Authors

    • G S. Gowri Department of Computer Science, Government Arts College, Coimbatore-641046
    • Dr. P. Ponmuthuramalingam LRG Government Arts College for Women, Tirupur
    2018-09-24
    https://doi.org/10.14419/ijet.v7i4.17610
  • Video-Based Object Extraction, Contour Tracking, Adaptive MRF, Video Segmentation, TEH-Chin Algorithm

  • Video object extraction (VOE) using segmentation from a video sequence is a very important task in editing and multimedia analysis for film making. Most of the VOE approaches required prior knowledge about background and foreground to extract target objects. In this paper, an Optimized smoothed Dirichlet Process Multi-view learning with improved adaptive Modified Markov Random Field which is enhanced by adaptive shape prior modified graph cut (OsDPMVL-IASMMRF) model has been extended for video-based object extraction. The contour tracking has been additionally included OsDPMVL-IASMMRF for VOE. The Teh–Chin algorithm has been used with OsDPMVL-IASMMRF for predicting the contour in the current frame by matching the extracted object contour from the previous segmented frame. The contour tracking propagates the shape of the target object, whereas the OsDPMVL-IASMMRF segmentation refined the object boundary and the shape for enhancing the accuracy of video segmentation. The experimental outcomes show that the proposed approach provides better segmentation results in terms of accuracy, precision and recall.

     

     

  • References

    1. [1] Bouwmans T (2014). Traditional and recent approaches in background modeling for foreground detection: An overview.Computer Science Review, 11, 31-66. https://doi.org/10.1016/j.cosrev.2014.04.001.

      [2] Vosters L, Shan C & Gritti T (2012). Real-time robust background subtraction under rapidly changing illumination conditions. Image and Vision Computing, 30(12), 1004-1015. https://doi.org/10.1016/j.imavis.2012.08.017.

      [3] Nikolov B & Kostov N (2014). Motion detection using adaptive temporal averaging method. Radioengineering, 23(2), 652-658.

      [4] Xue G, Sun J & Song L (2012). Background subtraction based on phase feature and distance transform. Pattern Recognition Letters, 33(12), 1601-1613. https://doi.org/10.1016/j.patrec.2012.05.009.

      [5] Fu Z & Han Y (2012). Centroid weighted Kalman filter for visual object tracking. Measurement, 45(4), 650-655. https://doi.org/10.1016/j.measurement.2012.01.004.

      [6] Weng SK, Kuo CM & Tu SK (2006). Video object tracking using adaptive Kalman filter. Journal of Visual Communication and Image Representation, 17(6), 1190-1208. https://doi.org/10.1016/j.jvcir.2006.03.004.

      [7] Zhang L, He X & Wang H (2012). Shadow Veriï¬cation Based on Feature Matching and Image Matting.

      [8] Ahuja K & Tuli P (2013). Object recognition by template matching using correlations and phase angle method.International Journal of Advanced Research in Computer and Communication Engineering, 2(3), 1368-1373.

      [9] Ruri S. Basuki, M. Hariadi, Eko M. Yuniarno, Mauridhi H. Purnomo. Spectral-Based Temporal-Constraint Estimation for Semi-Automatic Video Object Segmentation. International Review on Computers and Software (I.RE.CO.S.), Vol. 10, N. 9, 2015.

      [10] ZHANG L, WANG H, DENG T & HE X (2014). Improving integrality of detected moving objects based on image matting. Chinese Journal of Electronics, 23(4).

      [11] Chung CY & Chen HH (2010). Video object extraction via MRF-based contour tracking. IEEE Transactions on Circuits and Systems for Video Technology, 20(1), 149-155. https://doi.org/10.1109/TCSVT.2009.2026823.

      [12] Gowri GS & Ponmuthuramalingam P (2018). A SMOOTHED DPMVL FOR INTERACTIVE IMAGE SEGMENTATION AND ENHANCED ADAPTIVE MRF FOR SEGMENTATION REFINEMENT. PARIPEX - INDIAN JOURNAL OF RESEARCH, 7(4).

      [13] Teh CH & Chin RT (1989). On the detection of dominant points on digital curves. IEEE Transactions on pattern analysis and machine intelligence, 11(8), 859-872. https://doi.org/10.1109/34.31447.

      [14] Zhang XP & Chen Z (2006). An automated video object extraction system based on spatiotemporal independent component analysis and multiscale segmentation. EURASIP Journal on Advances in Signal Processing, 2006(1), 045217. https://doi.org/10.1155/ASP/2006/45217.

      [15] Lu Y & Li ZN (2008). Automatic object extraction and reconstruction in active video. Pattern Recognition, 41(3), 1159-1172. https://doi.org/10.1016/j.patcog.2007.07.015.

      [16] Mohammed US & Abd-Elhafiez WM (2009). A new video coding approach based on object-extraction. International Journal of Video & Image Processing and Network Security IJVIPNS-IJENS, 9(10), 62-70.

      [17] Manikandan R & Ramakrishnan R (2013). Video object extraction by using background subtraction techniques for sports applications. Digital Image Processing, 5(9), 435-440.

      [18] Wang H & Wang T (2016). Primary object discovery and segmentation in videos via graph-based transductive inference.Computer Vision and Image Understanding, 143, 159-172. https://doi.org/10.1016/j.cviu.2015.11.006.

      [19] Pun CM & Huang G (2016). On-line video object segmentation using illumination-invariant color-texture feature extraction and marker prediction. Journal of Visual Communication and Image Representation, 41, 391-405. https://doi.org/10.1016/j.jvcir.2016.10.017.

      [20] Hu YT, Huang JB & Schwing A (2017). MaskRNN: Instance Level Video Object Segmentation. In Advances in Neural Information Processing Systems (pp. 324-333).

      [21] Kanagamalliga S & Vasuki S (2018). Contour-based object tracking in video scenes through optical flow and gabor features. Optik-International Journal for Light and Electron Optics, 157, 787-797. https://doi.org/10.1016/j.ijleo.2017.11.181.

  • Downloads

  • How to Cite

    S. Gowri, G., & P. Ponmuthuramalingam, D. (2018). Video object extraction using optimized smoothed dirichlet process multi-view learning with improved adaptive modified Markova random field. International Journal of Engineering & Technology, 7(4), 2598-2602. https://doi.org/10.14419/ijet.v7i4.17610