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Segmenting images using localized histograms and region merging

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Abstract

A working system for segmenting images of complex scenes is presented. The system integrates techniques that have evolved out of many years of research in low-level image segmentation at the University of Massachusetts and elsewhere. This paper documents the result of this historical evolution. Segmentations produced by the system are used extensively in related image interpretation research.

The system first produces segmentations based upon an analysis of spatially localized feature histograms. These initial segmentations are then simplified using a region merging algorithm. Parameter selection for the local histogram segmentation algorithm is facilitated by mapping the multidimensional parameter space to a one-dimensional parameter which regulates region fragmentation. An extension of this algorithm to multiple features is also presented. Experience with roughly 100 images from different domains has shown the system to be robust and effective. Samples of these results are included.

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References

  1. D.H.Ballard and C.M.Brown, Computer Vision. Prentice-Hall: Englewood Cliffs, NJ, 1982.

    Google Scholar 

  2. H.G.Barrow and R.J.Popplestone, “Relational descriptions in picture processing.” In B.Meltzer and D.Michi, eds., Machine Intelligence 6. American Elsevier: New York, pp. 377–396, 1971.

    Google Scholar 

  3. R. Belknap, E. Riseman, and A. Hanson, “The information fusion problem and rule—based hypotheses applied to complex aggregations of image events,” CVPR-86 Proc., IEEE Computer Society, Miami, pp. 227–234, June 1986.

  4. J.R.Beveridge, J.Griffith, R.R.Kohler, A.R.Hanson, and F.M.Riseman, Segmenting Images Using Localized Histograms and Region Merging. Tech. Rept. 87–88, Dept. of Computer and Information Science, Univ. of Massachusetts, Amherst, October 1987.

    Google Scholar 

  5. B. Bhanu and O.D. Faugeras, “Segmentation of images having unimodal distributions,” IEEE Trans. PAMI-4(4): 408–419, July 1982.

    Google Scholar 

  6. C.R.Brice and C.L.Fennema, “Scene analysis using regions,” Artificial Intelligence 1:205–226, 1970.

    Google Scholar 

  7. J.D.Browning, “Segmentation of pictures int regions with a tile-by-tile method,” Pattern Recognition 15(1):1–10, 1982.

    Google Scholar 

  8. P.C. Chen and T. Pavlidis, “Segmentation by texture using a co-occurence matrix and a split-and-merge algorithm,” Fourth IJCPR, Kyoto, Japan, pp. 205–226, November 1978.

  9. LeeChin-Hwa, “Recursive region splitting at hierarchical scope views,” Computer Vision, Graphics, and Image Processing 33(2):237–258, February 1986.

    Google Scholar 

  10. C.K.Chow and T.Kaneko, “Boundary detection of radiographic images by a threshold method.” In S.Watanabe, ed., Frontiers of Pattern Recognition. Academic Press: New York, pp. 61–82, 1972.

    Google Scholar 

  11. G.B.Coleman and H.C.Andrews. “Image segmentation by clustering,” Proc. IEEE 67(5):773–785, 1979.

    Google Scholar 

  12. B.A. Draper, J. Brolio, R.T. Collins, A.R. Hanson, and E.M. Riseman, “Information fusion by distributed subsystems in a knowledge-based vision architecture,” CVPR-88 Proc., IEEE Computer Society, Ann Arbor, MI, June 1988, pp. 129–135.

  13. B.A.Draper, R.T.Collins, J.Brolio, J.Griffith, A.R.Hanson, and E.M.Riseman, “Tools and experiments in the knowledge-directed interpretation of road scenes,” Proc. DARPA Image Understanding Workshop, Morgan Kaufann: Los Altos, CA, pp. 178–193, February 1987.

    Google Scholar 

  14. B.A. Draper, R.T. Collins, J. Brolio, A.R. Hanson, and E.M. Riseman, “The schema system,” Int. J. Computer Vision 2(3), January 1989.

  15. R.O.Duda and P.Hart, Pattern Recognition and Scene Analysis. Wiley: New York, 1973.

    Google Scholar 

  16. J.A.Feldman and Y.Yakimovsky, “Decision theory and artificial intelligence i: a semantics-based region analyzer,” Artificial Intelligence 5:349–371, 1974.

    Google Scholar 

  17. O.Firshein and M.A.Fishler, “Describing and abstracting pictoral structures,” Pattern Recognition 3:421–444, 1971.

    Google Scholar 

  18. E.C.Freuder, “Affinity: a relative approach to region finding,” Computer Graphics and Image Processing 5:254–264, 1976.

    Google Scholar 

  19. A.R.Hanson and E.R.Riseman, “Segmentation of natural scenes.” In Hanson and Risman, eds., Computer Vision Systems. Academic Press: New York, pp. 129–163, 1978.

    Google Scholar 

  20. A.R.Hanson and E.M.Riseman, “The VISIONS Image Understanding System—1986,” Tech. Rept. 86–62, Dept. of Computer & Information Science, Univ. of Massachusetts, Amherst, December 1986.

    Google Scholar 

  21. A.R.Hanson, E.R.Riseman, and P.Nagin, “Region Growing in Textured Outdoor Scenes,” Tech. Rept. TR-75C-3, University of Massachusetts, Amherst, 1975.

    Google Scholar 

  22. A.R. Hanson, E.M. Riseman, and P.A. Nagin, “Authors reply to ‘image segmentation: a comment on studies in global and local histogram-guided relaxation algorithms’,” IEEE Trans. PAMI 6(2), March 1984.

  23. R.M.Haralick and L.G.Shapiro, “Image segmentation techniques,” Computer Vision, Graphics, and Image Processing 29:100–132, 1985.

    Google Scholar 

  24. R.M. Haralick and L. Watson, “A facet model for image data,” Proc. Pattern Recog. and Image Processing, Chicago, pp. 489–497, 1979.

  25. M.D.Kelly, “Edge detection in pictures by computer using planning.” In B.Meltzer and D.Michi, eds., Machine Intelligence 6. American Elsevier: New York, pp. 379–409, 1971.

    Google Scholar 

  26. J.Kittler and J.Illingworth, “On threshold selection using clustering criteria,” IEEE Trans. Syst., Man, Cybern. SMC—15(5):652–655, 1985.

    Google Scholar 

  27. A. Klinger, “Data structures and pattern recognition,” Proc. 1st Int. Joint Conf. Pattern Recognition, Washington, D.C., pp. 497–498, 1973.

  28. A.Klinger and C.R.Dyer, “Experiments on picture representation using regular decomposition,” Computer Graphics and Image Processing 5(1):68–105, 1976.

    Google Scholar 

  29. C. Kohl, GOLDIE: A Goal-Directed Intermediate-Level Executive for Image Interpretation. PhD Thesis, Univ. of Massachusetts, Amherst, 1988.

  30. C.Kohl, A.Hanson, and E.Riseman, “A goal-directed intermediate level executive for image interpretation,” Proc. 10th Int. Joint Conf. Artif. Intel., Morgan Kaufman: Los Altos, CA, pp. 811–814, August 1987.

    Google Scholar 

  31. C.A.Kohl, A.R.Hanson, and E.M.Riseman, “Goaldirected control of low-level processes for image interpretation,” Proc. DARPA Image Understanding Workshop, Morgan Kaufman: New York, Los Angeles, pp. 538–551, February 1987.

    Google Scholar 

  32. R.R. Kohler, “Integrating Non-Semantic Knowledge into Image Segmentation Processes.” PhD thesis, COINS, Univ. of Massachusetts, Amherst, 1984.

  33. N.Lehrer, G.Reynolds, and J.Griffith, “A method for initial hypothesis formation in image understanding,” Proc. DARPA Image Understanding Workshop, Morgan Kaufmann: Los Altos, CA, pp. 521–537, February 1987.

    Google Scholar 

  34. M.D.Levine and A.M.Nazif, “Dynamic measurement of computer generated image segmentations,” IEEE Trans. PAMI-7(2):155–164, March 1985.

    Google Scholar 

  35. M.Nagao and T.Matsuyama, A Structural Analysis of Complex Aerial Photographs. Plenum Press: New York, 1980.

    Google Scholar 

  36. P.A.Nagin, “Studies in image segmentation algorithms based on histogram clustering and relaxation,” Tech. Rept. 79–15, Univ. of Massachusetts, Amherst, September 1979.

    Google Scholar 

  37. P.A. Nagin, A.R. Hanson, and E.M. Riseman, “Segmentation, evaluation, and natural scenes,” Proc. IEEE Conf. on Pattern Recognition and Image Processing, IEEE Computer Society, pp. 515–522, August 1979.

  38. P.A. Nagin, A.R. Hanson, and E.M. Riseman, “Studies in global and local histogram-guided relaxation algorithms,” IEEE Trans. PAMI-4(3), May 1982.

  39. A.M. Nazif, “A Rule-Based Expert System for Image Segmentation.” PhD thesis, Electrical Engineering Department, McGill University, 1983.

  40. R.B. Ohlander, “Analysis of Natural Scenes.” PhD thesis, CMU, Pittsburgh PA, 1975.

  41. R.B. Ohlander, K.E. Price, and D.R. Reddy, “Picture segmentation using a recursive region splitting method,” Computer Graphics and Image Processing 3(8), 1979.

  42. Y.Ohta, T.Kanade, and T.Sakai, “Color information for region segmentation,” Computer Graphics and Image Processing 13:222–241, 1980.

    Google Scholar 

  43. N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. Syst., Man, Cybern. SMC-9(1), January 1979.

    Google Scholar 

  44. K.J. Overton and T.E. Weymouth,” A noise reducing preprocessing algorithm,” Proc. Pattern Recog. and Image Processing, Chicago, pp. 498–507, 1979.

  45. T.Pavlidis, Structural Pattern Recognition. Springer: New York, 1977.

    Google Scholar 

  46. S.Peleg and A.Rosenfeld, “Determining compatibility coefficients for curve enhancement relaxation processes,” IEEE Trans. Syst., Man, Cybern. SMC-8:548–555, 1978.

    Google Scholar 

  47. J.S.M.Prewitt and M.L.Mendelsohn, “The analysis of cell images,” Anal. N.Y. Acad. Sci., 128:1035–1053, 1966.

    Google Scholar 

  48. K.E. Price, “Image segmentation: a comment on studies in global and local histogram-guided relaxation algorithms,” IEEE Trans. PAMI 6(2), March 1984.

  49. S.S.Reddi, S.F.Rudin, and H.R.Keshavan, “An optimal multiple threshold scheme for image segmentation,” IEEE Trans. Syst., Man, Cybern. SMC-14(4):661–664, July/August 1984.

    Google Scholar 

  50. G.Reynolds and J.R.Beveridge, “Searching for geometric structure in images of natural scenes,” Proc. DARPA Image Understanding Workshop, Morgan Kaufmann: Los Altos, CA, pp. 257–271, February 1987.

    Google Scholar 

  51. G.Reynolds, N.Irwin, A.Hanson, and E.Riseman, “Hierarchical knowledge-directed object extraction using a combined region and line representation,” IEEE Proc. Workshop on Computer Vision Representation and Control, IEEE Computer Society Press: Silver Spring, MD, pp. 238–247, 1984.

    Google Scholar 

  52. A.Rosenfeld, “Interative methods in image analysis,” Pattern Recognition, 10:181–187, 1978.

    Google Scholar 

  53. A.Rosenfeld and A.C.Kak, Digital Picture Processing. Academic Press: New York, 1976.

    Google Scholar 

  54. A.Rosenfeld, R.Hummel, and S.Zucker, “Scene labeling by relaxation operators,” IEEE Trans. Syst., Man, Cybern., SMC-6:420–433, 1976.

    Google Scholar 

  55. H. Shvayster and S. Peleg, “A new approach to the consistent labeling problem,” Proc. CVPR-85, IEEE Computer Society, pp. 320–327, June 1985.

  56. J.M.Tenenbaum and H.G.Barrow, “Experiments in interpretation guided segmentation,” Artificial Intelligence 8:241–274, 1976.

    Google Scholar 

  57. R.Weiss, L.Kitchen, and J.Tuttle. “Identification of Human Faces Using Data-Driven Segmentation, Rule-Based Hypothesis Formation, and Iterative Model-Based Hypothesis Verification,” Tech. Rept. 86–53, Univ. of Massachusetts, Amherst, October 1986.

    Google Scholar 

  58. T.E. Weymouth, “Using Object Descriptions In A Schema Network For Machine Vision.” PhD thesis, Univ. of Massachusetts, Amherst, MA, January 1986.

  59. Y.Yakimovsky, “Boundary and object detection in real world images,” J. Assoc. Computing Machinery 23(4):599–618, October 1976.

    Google Scholar 

  60. S.W.Zucker, “Region growing: childhood and adolescence,” Computer Graphics and Image Processing 5:382–399, 1976.

    Google Scholar 

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Authors and Affiliations

  1. Computer and Information Science, University of Massachusetts, Amherst, USA

    J. Ross Beveridge, Joey Griffith, Ralf R. Kohler, Allen R. Hanson & Edward M. Riseman

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  1. J. Ross Beveridge
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  2. Joey Griffith
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  3. Ralf R. Kohler
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  5. Edward M. Riseman
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Beveridge, J.R., Griffith, J., Kohler, R.R. et al. Segmenting images using localized histograms and region merging. Int J Comput Vision 2, 311–347 (1989). https://doi.org/10.1007/BF00158168

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