Data Association via Set Packing for Computer Vision Applications

Published Online:https://doi.org/10.1287/ijoo.2019.0030

References

  • Andres B, Kappes JH, Beier T, Kothe U, Hamprecht FA (2011) Probabilistic image segmentation with closedness constraints. Proc. 13th Internat. Conf. Comput. Vision (IEEE, Piscataway, NJ), 2611–2618.Google Scholar
  • Andriluka M, Pishchulin L, Gehler P, Schiele B (2014) 2D human pose estimation: New benchmark and state of the art analysis. Proc. 27th Conf. Comput. Vision Pattern Recognition (IEEE, Piscataway, NJ), 3686–3693.Google Scholar
  • Arteta C, Lempitsky V, Noble J, Zisserman A (2012) Learning to detect cells using non-overlapping extremal regions. Proc. 15th Internat. Conf. Medical Image Comput. Comput.-Assisted Intervention (Springer, Berlin, Heidelberg), 348–356.Google Scholar
  • Arteta C, Lempitsky V, Noble J, Zisserman A (2016) Detecting overlapping instances in microscopy images using extremal region trees. Medical Image Anal. 27:3–16.Google Scholar
  • Bansal N, Blum A, Chawla S (2004) Correlation clustering. J. Machine Learning 56(1–3):89–113.Google Scholar
  • Barnhart C, Johnson EL, Nemhauser GL, Savelsbergh MWP, Vance PH (1996) Branch-and-price: Column generation for solving huge integer programs. Oper. Res. 46:316–329.LinkGoogle Scholar
  • Ben Amor H, Desrosiers J, Valério de Carvalho JM (2006) Dual-optimal inequalities for stabilized column generation. Oper. Res. 54(3):454–463.LinkGoogle Scholar
  • Benders JF (1962) Partitioning procedures for solving mixed-variables programming problems. Numerische Math. 4(1):238–252.Google Scholar
  • Bernardin K, Stiefelhagen R (2008) Evaluating multiple object tracking performance: The CLEAR MOT metrics. EURASIP J. Image Video Processing 2008:246309.Google Scholar
  • Birge JR (1985) Decomposition and partitioning methods for multistage stochastic linear programs. Oper. Res. 33(5):989–1007.LinkGoogle Scholar
  • Boykov Y, Kolmogorov V (2004) An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Machine Intelligence 26(9):1124–1137.Google Scholar
  • Butt A, Collins R (2013) Multi-target tracking by Lagrangian relaxation to min-cost network flow. Proc. 26th Conf. Comput. Vision Pattern Recognition (IEEE, Piscataway, NJ), 1846–1853.Google Scholar
  • Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. Proc. 18th Conf. Comput. Vision Pattern Recognition, vol. 1 (IEEE, Piscataway, NJ), 886–893.Google Scholar
  • Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) ImageNet: A large-scale hierarchical image database. Proc. 22nd Conf. Comput. Vision Pattern Recognition (IEEE, Piscataway, NJ), 248–255.Google Scholar
  • Desai C, Ramanan D, Fowlkes CC (2011) Discriminative models for multi-class object layout. Internat. J. Comput. Vision 95(1):1–12.Google Scholar
  • Desaulniers G, Desrosiers J, Solomon MM, eds. (2005) Column Generation, 1st ed. (Springer, New York).Google Scholar
  • Desrosiers J, Lübbecke ME (2005) A primer in column generation. Desaulniers G, Desrosiers J, Solomon MM, eds. Column Generation (Springer, New York), 1–32.Google Scholar
  • Dimopoulos S, Mayer C, Rudolf F, Stelling J (2014) Accurate cell segmentation in microscopy images using membrane patterns. Bioinformatics 30(18):2644–2651.Google Scholar
  • Felzenszwalb P, McAllester D, Ramanan D (2008) A discriminatively trained, multiscale, deformable part model. Proc. 30th Conf. Comput. Vision Pattern Recognition (IEEE, Piscataway, NJ), 1–8.Google Scholar
  • Funke J, Hamprecht F, Zhang C (2015) Learning to segment: Training hierarchical segmentation under a topological loss. Navab N, Hornegger J, Wells W, Frangi A, eds. Proc. 18th Internat. Conf. Medical Image Comput. Comput.-Assisted Intervention, Lecture Notes in Computer Science, vol. 9351 (Springer, Cham, Switzerland), 268–275.Google Scholar
  • Gilmore P, Gomory R (1961) A linear programming approach to the cutting-stock problem. Oper. Res. 9(6):849–859.LinkGoogle Scholar
  • Gschwind T, Irnich S (2016) Dual inequalities for stabilized column generation revisited. INFORMS J. Comput. 28(1):175–194.LinkGoogle Scholar
  • Hilsenbeck O, Schwarzfischer M, Loeffler D, Dimopoulos S, Hastreiter S, Marr C, Theis F, Schroeder T (2017) fastER: A user-friendly tool for ultrafast and robust cell segmentation in large-scale microscopy. Bioinformatics 33(13):2020–2028.Google Scholar
  • Insafutdinov E, Pishchulin L, Andres B, Andriluka M, Schiele B (2016) Deepercut: A deeper, stronger, and faster multi-person pose estimation model. Proc. 14th Eur. Conf. Computer Vision (Springer, Cham, Switzerland), 34–50.Google Scholar
  • Jepsen M, Petersen B, Spoorendonk S, Pisinger D (2008) Subset-row inequalities applied to the vehicle-routing problem with time windows. Oper. Res. 56(2):497–511.LinkGoogle Scholar
  • Joncour C, Michel S, Sadykov R, Vanderbeck F (2010) Column generation based primal heuristics. Electronic Notes Discrete Math. 36:695–702.Google Scholar
  • Kappes JH, Speth M, Reinelt G, Schnörr C (2016) Higher-order segmentation via multicuts. Comput. Visage Image Understanding 143(C):104–119.Google Scholar
  • Karp RM (1972) Reducibility among combinatorial problems. Proc. Sympos. Complexity Comput. Comput. (Springer, Boston), 85–103.Google Scholar
  • Kolmogorov V (2006) Convergent tree-reweighted message passing for energy minimization. IEEE Trans. Pattern Anal. Machine Intelligence 28(10):1568–1583.Google Scholar
  • Komodakis N, Paragios N, Tziritas G (2007) MRF optimization via dual decomposition: Message-passing revisited. Proc. 11th Internat. Conf. Comput. Vision (IEEE, Piscataway, NJ), 1–8.Google Scholar
  • Leal-Taixe L, Pons-Moll G, Rosenhahn B (2012) Branch-and-price global optimization for multi-view multi-target tracking. Proc. 25th Conf. Comput. Vision Pattern Recognition (IEEE, Piscataway, NJ), 1987–1994.Google Scholar
  • Leal-Taixé L, Milan A, Reid I, Roth S, Schindler K (2015) MOT Challenge 2015: Toward a benchmark for multi-target tracking. Preprint, submitted April 8, https://arxiv.org/abs/1504.01942.Google Scholar
  • Levinkov E, Uhrig J, Tang S, Omran M, Insafutdinov E, Kirillov A, Rother C, Brox T, Schiele B, Andres B (2017) Joint graph decomposition and node labeling: Problem, algorithms, applications. Proc. 30th Conf. Comput. Vision Pattern Recognition (IEEE, Piscataway, NJ), 6012–6020.Google Scholar
  • Pishchulin L, Insafutdinov E, Tang S, Andres B, Andriluka M, Gehler PV, Schiele B (2016) Deepcut: Joint subset partition and labeling for multi person pose estimation. Proc. 22nd Conf. Comput. Vision Pattern Recognition (IEEE, Piscataway, NJ), 4929–4937.Google Scholar
  • Ren X, Malik J (2003) Learning a classification model for segmentation. Proc. 16th Internat. Conf. Comput. Vision Pattern Recognition (IEEE, Piscataway, NJ), 10–17.Google Scholar
  • Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. Proc. 18th Internat. Conf. Medical Image Comput. Comput.-Assisted Intervention (Springer, Cham, Switzerland), 234–241.Google Scholar
  • Rumelhart DE, Hinton GE, Williams RJ (1985) Learning internal representations by error propagation. Technical report, Institute for Cognitive Science, University of California, San Diego, La Jolla.Google Scholar
  • Silberman N, Sontag D, Fergus R (2014) Instance segmentation of indoor scenes using a coverage loss. Fleet D, Pajdla T, Schiele B, Tuytelaars T, eds. Proc. 14th Eur. Conf. Comput. Vision, Lecture Notes in Computer Science, vol. 8689 (Springer, Cham, Switzerland), 616–631.Google Scholar
  • Sommer C, Straehle C, Koethe U, Hamprecht FA (2011) Ilastik: Interactive learning and segmentation toolkit. Proc. 8th Internat. Sympos. Biomedical Imaging (IEEE, Piscataway, NJ), 230–233.Google Scholar
  • Sontag D, Meltzer T, Globerson A, Jaakkola T, Weiss Y (2008) Tightening LP relaxations for MAP using message passing. Proc. 24th Conf. Uncertainty Artificial Intelligence (AUAI Press, Arlington, VA), 503–510.Google Scholar
  • Tsochantaridis I, Joachims T, Hofmann T, Altun Y (2005) Large margin methods for structured and interdependent output variables. J. Machine Learning Res. 6:1453–1484.Google Scholar
  • Valério de Carvalho JM (2005) Using extra dual cuts to accelerate column generation. INFORMS J. Comput. 17(2):175–182.LinkGoogle Scholar
  • Wang S, Fowlkes C (2015) Learning optimal parameters for multi-target tracking. Proc. 26th British Machine Vision Conf. (BMVA Press, UK),484–501.Google Scholar
  • Wang S, Kording K, Yarkony J (2017a) Exploiting skeletal structure in computer vision annotation with Benders decomposition. Preprint, submitted September 13, https://arxiv.org/abs/1709.04411.Google Scholar
  • Wang S, Ihler A, Kording K, Yarkony J (2018) Accelerating dynamic programs via nested benders decomposition with application to multi-person pose estimation. Proc. 15th Eur. Conf. Comput. Vision (Springer, Cham, Switzerland), 652–666.Google Scholar
  • Wang S, Wolf S, Fowlkes C, Yarkony J (2017b) Tracking objects with higher order interactions via delayed column generation. Proc. 20th Internat. Conf. Artificial Intelligence Statist. (PMLR), 1132–1140.Google Scholar
  • Wang S, Zhang C, Gonzalez-Ballester MA, Ihler A, Yarkony J (2017c) Multi-person pose estimation via column generation. Preprint, submitted September 18, https://arxiv.org/abs/1709.05982.Google Scholar
  • Yarkony J, Fowlkes C (2015) Planar ultrametrics for image segmentation. Proc. 28th Advances in Neural Information Processing Systems (MIT Press, Cambridge, MA), 64–72.Google Scholar
  • Yarkony J, Ihler A, Fowlkes C (2012) Fast planar correlation clustering for image segmentation. Proc. 12th Eur. Conf. Comput. Vision (Springer, Cham, Switzerland), 1169–1176.Google Scholar
  • Yu CN, Joachims T (2009) Learning structural SVMs with latent variables. Proc. 26th Internat. Conf. Machine Learn. (ACM, New York), 1169–1176.Google Scholar
  • Zhang C, Yarkony J, Hamprecht FA (2014a) Cell detection and segmentation using correlation clustering. Proc. 17th Internat. Conf. Medical Image Comput. Comput.-Assisted Intervention (Springer, Cham, Switzerland), 9–16.Google Scholar
  • Zhang C, Huber F, Knop M, Hamprecht FA (2014b) Yeast cell detection and segmentation in bright field microscopy. Proc. 11th Internat. Sympos. on Biomedical Imaging (IEEE, Piscataway, NJ), 1267–1270.Google Scholar
  • Zhang C, Wang S, Gonzalez-Ballester MA, Yarkony J (2017) Efficient column generation for cell detection and segmentation. Preprint, submitted September 21, https://arxiv.org/abs/1709.07337.Google Scholar
  • Zhang L, Li Y, Nevatia R (2008) Global data association for multi-object tracking using network flows. Proc. 21st Conf. Comput. Vision Pattern Recognition (IEEE, Piscataway, NJ), 1–8.Google Scholar
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