Skip to main content
SpringerLink
Log in

An Approach to the Parameterization of Structure for Fast Categorization

  • Published:
International Journal of Computer Vision Aims and scope Submit manuscript

Abstract

A decomposition is described, which parameterizes the geometry and appearance of contours and regions of gray-scale images with the goal of fast categorization. To express the contour geometry, a contour is transformed into a local/global space, from which parameters are derived classifying its global geometry (arc, inflexion or alternating) and describing its local aspects (degree of curvature, edginess, symmetry). Regions are parameterized based on their symmetric axes, which are evolved with a wave-propagation process enabling to generate the distance map for fragmented contour images. The methodology is evaluated on three image sets, the Caltech 101 set and two sets drawn from the Corel collection. The performance nearly reaches the one of other categorization systems for unsupervised learning.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Amadasun, M., & King, R. (1989). Textural features corresponding to textural properties. IEEE Transactions on Systems, Man, and Cybernetics, 19, 1264–1274.

    Article  Google Scholar 

  • Amit, Y., & Mascaro, M. (2003). An integrated network for invariant visual detection and recognition. Vision Research, 43(19), 2073–2088.

    Article  Google Scholar 

  • Asada, H., & Brady, M. (1986). The curvature primal sketch. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8, 2–14.

    Article  Google Scholar 

  • Basri, R., & Jacobs, D. (1997). Recognition using region correspondences. International Journal of Computer Vision, 25(2), 145–166.

    Article  Google Scholar 

  • Bengtsson, A., & Eklundh, J.-O. (1991). Shape representation by multiscale contour approximation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, 85–93.

    Article  Google Scholar 

  • Berengolts, A., & Lindenbaum, M. (2006). On the distribution of saliency. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12), 1973–1990.

    Article  Google Scholar 

  • Berretti, S., Del Bimbo, A., & Pala, P. (2000). Retrieval by shape similarity with perceptual distance and effective indexing. IEEE Transactions on Multimedia, 2, 225–239.

    Article  Google Scholar 

  • Biederman, I. (1987). Recognition by components: a theory of human image understanding. Psychological Review, 94, 115–145.

    Article  Google Scholar 

  • Biederman, I., Mezzanotte, R., & Rabinowitz, J. (1982). Scene perception: detecting and judging objects undergoing relational violations. Cognitive Psychology, 14, 143–177.

    Article  Google Scholar 

  • Blum, H. (1967). A new model of global brain function. Perspectives in Biology and Medicine, 10(3), 381–384.

    MathSciNet  Google Scholar 

  • Blum, H. (1973). Biological shape and visual science 1. Journal of Theoretical Biology, 38(2), 205–287.

    Article  MathSciNet  Google Scholar 

  • Brady, T. F., Konkle, T., Alvarez, G. A., & Oliva, A. (2008). Visual long-term memory has a massive storage capacity for object details. PNAS Proceedings of the National Academy of Sciences, 105(38), 14325–14329.

    Article  Google Scholar 

  • Brooks, R. (1981). Symbolic reasoning among 3-d models and 2-d images. Artificial Intelligence, 17, 285–348.

    Article  Google Scholar 

  • Canny, J. (1986). A computational approach to edge-detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6), 679–698.

    Article  Google Scholar 

  • Demirci, M., Shokoufandeh, A., Keselman, Y., Bretzner, L., & Dickinson, S. (2006). Object recognition as many-to-many feature matching. International Journal of Computer Vision, 69(2), 203–222.

    Article  Google Scholar 

  • Draper, B., Hanson, A., & Riseman, E. (1996). Knowledge-directed vision: Control, learning, and integration. Proceedings of the IEEE, 84(11), 1625–1637.

    Article  Google Scholar 

  • Dror, R., Willsky, A. S., & Adelson, E. H. (2004). Statistical characterization of real-world illumination. Journal of Vision, 4(9), 821–837.

    Article  Google Scholar 

  • Dudek, G., & Tsotsos, J. (1997). Shape representation and recognition from multiscale curvature. Computer Vision and Image Understanding, 68, 170–189.

    Article  Google Scholar 

  • Elder, J. (1999). Are edges incomplete? International Journal of Computer Vision, 34(2–3), 97–122.

    Article  Google Scholar 

  • Elder, J., Krupnik, A., & Johnston, L. (2003). Contour grouping with prior models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(6), 661–674.

    Article  Google Scholar 

  • Fabbri, R., Da, F., Costa, L., Torelli, J. C., & Bruno, O. M. (2008). 2d euclidean distance transform algorithms: A comparative survey. ACM Computing Surveys, 40(1), 2:1–2:44. Article 2.

    Article  Google Scholar 

  • Felzenszwalb, P., & Huttenlocher, D. (2005). Pictorial structures for object recognition. International Journal of Computer Vision, 61(1), 55–79.

    Article  Google Scholar 

  • Felzenszwalb, P. F., & Schwartz, J. D. (2007). Hierarchical matching of deformable shapes. In IEEE conference on computer vision and pattern recognition (pp. 1–8). Minneapolis, USA, 17–22 June 2007.

  • Fergus, R., Perona, P., & Zisserman, A. (2004). A visual category filter for Google images. European Conference on Computer Vision 2004, PT1, 3021, 242–256.

    Google Scholar 

  • Fergus, R., Perona, P., & Zisserman, A. (2007). Weakly supervised scale-invariant learning of models for visual recognition. International Journal of Computer Vision, 71(3), 273–303.

    Article  Google Scholar 

  • Fischler, M., & Bolles, R. (1983). Perceptual organization and the curve partitioning problem. In Proceedings of the tenth international joint conference on artificial intelligence (Vol. 2).

  • Fonseca, M., Ferreira, A., & Jorge, J. (2006). Generic shape classification for retrieval. In Graphics recognition. Ten years review and future perspectives. Berlin: Springer.

    Google Scholar 

  • Fu, K. (1968). Sequential methods in pattern recognition and machine learning. London: Academic Press.

    MATH  Google Scholar 

  • Gunther, O., & Wong, E. (1990). The arc tree: An approximation scheme to represent arbitrary curved shapes. Computer Vision, Graphics and Image Processing, 51, 313–337.

    Article  Google Scholar 

  • Gregory, R. (1997). Knowledge in perception and illusion. Philosophical Transactions of the Royal Society of London, Series B, Biological Sciences, 352(1358), 1121–1127.

    Article  Google Scholar 

  • Hansen, T., & Neumann, H. (2004). Neural mechanisms for the robust representation of junctions. Neural Computation, 16(5), 1013–1037.

    Article  MATH  Google Scholar 

  • Haralick, R. M. (1979). Statistical and structural approaches to texture. Proceedings of the IEEE, 67, 786–804.

    Article  Google Scholar 

  • Heidemann, G. (2005). Unsupervised image categorization. Image and Vision Computing, 23(10), 861–876.

    Article  Google Scholar 

  • Heitz, G., Elidan, G., Packer, B., & Koller, D. (2009). Shape-based object localization for descriptive classification. International Journal of Computer Vision, 84, 40–62.

    Article  Google Scholar 

  • Itti, L., Koch, C., & Niebur, E. (1998). A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11), 1254–1259.

    Article  Google Scholar 

  • Jolicoeur, P., Gluck, M., & Kosslyn, M. (1984). Pictures and names: Making the connection. Cognitive Psychology, 16, 243–275.

    Article  Google Scholar 

  • Joubert, O. R., Rousselet, G. A., Fize, D., & Fabre-Thorpe, M. (2008). Processing scene context: fast categorization and object interference. Vision Research, 47(26), 3286–3297.

    Article  Google Scholar 

  • Kadir, T., & Brady, M. (2001). Saliency, scale and image description. International Journal of Computer Vision, 45(2), 83–105.

    Article  MATH  Google Scholar 

  • Keselman, Y., & Dickinson, S. (2005). Generic model abstraction from examples. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, 1141–1156.

    Article  Google Scholar 

  • Kimia, B., Tannenbaum, A., & Zucker, S. (1995). Shapes, shocks, and deformations I: The components of two-dimensional shape and the reaction-diffusion space. International Journal of Computer Vision, 15(3), 189–224.

    Article  Google Scholar 

  • Leibe, B., & Schiele, B. (2003). Analyzing appearance and contour based methods for object categorization. In IEEE computer society conference on computer vision and pattern recognition, Madison, USA, June 2003.

  • Li, F., Fergus, R., & Perona, P. (2006). One-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(4), 594–611.

    Article  Google Scholar 

  • Lowe, D. G. (1985). Perceptual organization and visual recognition. Boston: Kluwer Academic.

    Google Scholar 

  • Malik, J., Belongie, S., Leung, T., & Shi, J. (2001). Contour and texture analysis for image segmentation. International Journal of Computer Vision, 43(1), 7–27.

    Article  MATH  Google Scholar 

  • Marr, D. (1982). Vision. New York: Freeman.

    Google Scholar 

  • Martin, D., Fowlkes, C., & Malik, J. (2004). Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(5), 530–549.

    Article  Google Scholar 

  • Mel, B. W. (1997). Seemore: combining color, shape, and texture histogramming in a neurally inspired approach to visual object recognition. Neural Computation, 9(4), 777–804.

    Article  Google Scholar 

  • Minsky, M. (1975). A framework for representing knowledge. In P. Winston (Ed.), The psychology of computer vision (pp. 211–277). New York: McGraw-Hill.

    Google Scholar 

  • Mojsilovic, A., Gomes, J., & Rogowitz, B. (2004). Semantic-friendly indexing and querying of images based on the extraction of the objective semantic cues. International Journal of Computer Vision, 56(1–2), 79–107.

    Article  Google Scholar 

  • Mokhtarian, F., & Mackworth, A. (1986). Scale-based description and recognition of planar curves and two-dimensional shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8, 34–43.

    Article  Google Scholar 

  • Mokhtarian, F., & Mackworth, A. (1992). A theory of multiscale, curvature-based shape representation for planar curves. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14, 789–805.

    Article  Google Scholar 

  • Mori, G., Belongie, S., & Malik, J. (2005). Efficient shape matching using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(11), 1832–1837.

    Article  Google Scholar 

  • Motoyoshi, I., Nishida, S., Sharan, L., & Adelson, E. H. (2007). Image statistics and the perception of surface qualities. Nature, 447(7141), 206–209.

    Article  Google Scholar 

  • Nelson, R., & Selinger, A. (1998). A cubist approach to object recognition. In Sixth international conference on computer vision.

  • Niethammer, M., Betelu, S., Sapiro, G., Tannenbaum, A., & Giblin, P. (2004). Area-based medial axis of planar curves. International Journal of Computer Vision, 60(3), 203–224.

    Article  Google Scholar 

  • Noton, D., & Stark, L. (1971). Scanpaths in eye movements during pattern perception. Science, 171, 308–311.

    Article  Google Scholar 

  • Novak, D., Batko, M., & Zezula, P. (2008). Web-scale system for image similarity search: When the dreams are coming true. In IEEE CBMI 2008 (pp. 446–453). London.

  • Ogniewicz, R., & Kubler, O. (1995). Voronoi tessellation of points with integer coordinates: Time-efficient implementation and online edge-list generation. Pattern Recognition, 28(12), 1839–1844.

    Article  Google Scholar 

  • Oliva, A., & Torralba, A. (2001). Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision, 42(3), 145–175.

    Article  MATH  Google Scholar 

  • Opelt, A., Pinz, A., & Zisserman, A. (2006). A boundary-fragment-model for object detection. In LNCS : Vol. 3952. European conference on computer vision (ECCV 2006), Part II. Berlin: Springer.

    Google Scholar 

  • Palmer, S. E. (1999). Vision science: photons to phenomenology. Cambridge: MIT Press.

    Google Scholar 

  • Palmer, S. E., Rosch, E., & Chase, P. (1981). Canonical perspective and the perception of objects. In J. Long & A. Baddeley (Eds.), Attention and performance IX (pp. 135–151). Hillsdale: Erlbaum.

    Google Scholar 

  • Parent, P., & Zucker, S. W. (1989). Trace inference, curvature consistency, and curve detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, 823–839.

    Article  Google Scholar 

  • Pelillo, M., Siddiqi, K., & Zucker, S. (1999). Matching hierarchical structures using association graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21, 1105–1120.

    Article  Google Scholar 

  • Potter, M. C. (1976). Short-term conceptual memory for pictures. Journal of Experimental Psychology (Human Learning), 2(5), 509–522.

    MathSciNet  Google Scholar 

  • Privitera, C., & Stark, L. (2000). Algorithms for defining visual regions-of-interest: Comparison with eye-fixations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 970–981.

    Article  Google Scholar 

  • Rajashekar, U., Van der Linde, I., Bovik, A., & Cormack, L. (2008). Gaffe: A gaze-attentive fixation finding engine. IEEE Transactions on Image Processing, 17, 564–573. http://live.ece.utexas.edu/research/gaffe.

    Article  MathSciNet  Google Scholar 

  • Rasche, C. (2005). The making of a neuromorphic visual system. Berlin: Springer.

    Google Scholar 

  • Rasche, C. (2007). Neuromorphic excitable maps for visual processing. IEEE Transactions on Neural Networks, 18(2), 520–529.

    Article  Google Scholar 

  • Ravishankar Rao, A., & Lohse, G. L. (1993). Identifying high level features of texture perception. CVGIP: Graphical Models and Image Processing, 55, 218–233.

    Google Scholar 

  • Renninger, L., & Malik, J. (2004). When is scene identification just texture recognition? Vision Research, 44(19), 2301–2311.

    Google Scholar 

  • Rolls, E., & Deco, G. (2002). Computational neuroscience of vision. New York: Oxford University Press.

    Google Scholar 

  • Rosch, E., Mervis, C., Gray, W., & Boyes-Braem, P. (1976). Basic objects in natural categories. Cognitive Psychology, 8, 382–439.

    Article  Google Scholar 

  • Rosenfeld, A., & Pfaltz, J. (1968). Distance functions on digital pictures. Pattern Recognition, 1(1), 33–61.

    Article  MathSciNet  Google Scholar 

  • Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M., & Poggio, T. (2007). Robust object recognition with cortex-like mechanisms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(3), 411–426.

    Article  Google Scholar 

  • Shotton, J., Blake, A., & Cipolla, R. (2005). Contour-based learning for object detection. In The 10th IEEE international conference on computer vision (ICCV05) (pp. 503–510).

  • Shotton, J., Blake, A., & Cipolla, R. (2008). Multi-scale categorical object recognition using contour fragments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(7), 1270–1281.

    Article  Google Scholar 

  • Siddiqi, K., Kimia, B., Tannenbaum, A., & Zucker, S. (1999). Shapes, shocks and wiggles. Image and Vision Computing, 17(5–6), 365–373.

    Article  Google Scholar 

  • Standing, L., Conezio, J., & Haber, R. N. (1970). Perception and memory for pictures—single-trial learning of 2500 visual stimuli. Psychonomic Science, 19(2), 73–74.

    Google Scholar 

  • Sudderth, E., Torralba, A., Freeman, W., & Willsky, A. (2008). Describing visual scenes using transformed objects and parts. International Journal of Computer Vision, 77, 291–330.

    Article  Google Scholar 

  • Tamura, H., Mori, S., & Yamawaki, T. (1978). Textural features corresponding to visual perception. IEEE Transactions on Systems, Man, and Cybernetics, 8, 460–473.

    Article  Google Scholar 

  • Thorpe, S., Fize, D., & Marlot, C. (1996). Speed of processing in the human visual system. Nature, 381, 520–522.

    Article  Google Scholar 

  • Torralba, A., Oliva, A., Castelhano, M., & Henderson, J. (2006). Contextual guidance of eye movements and attention in real-world scene: The role of global features on object search. Psychological Review, 113, 766–786.

    Article  Google Scholar 

  • Treisman, A., & Gormican, S. (1988). Feature analysis in early vision: evidence from search asymmetries. Psychological Review, 95(1), 15–48.

    Article  Google Scholar 

  • Tu, Z., & Zhu, S. (2006). Parsing images into regions, curves, and curve groups. International Journal of Computer Vision, 69(2), 223–249.

    Article  Google Scholar 

  • VanRullen, R., & Thorpe, S. J. (2002). Surfing a spike wave down the ventral stream. Vision Research, 42(23), 2593–2615.

    Article  Google Scholar 

  • Vogel, J., & Schiele, B. (2007). Semantic modeling of natural scenes for content-based image retrieval. International Journal of Computer Vision, 72(2), 133–157.

    Article  Google Scholar 

  • Wang, J., Li, J., & Gio, W. (2001). Simplicity: Semantics-sensitive integrated matching for picture libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(9), 947–963.

    Article  Google Scholar 

  • Wang, S., Stahl, J., Bailey, A., & Dropps, M. (2007). Global detection of salient convex boundaries. International Journal of Computer Vision, 71, 337–359.

    Article  Google Scholar 

  • Witkin, A., & Tenenbaum, J. (1983). On the role of structure in vision. In J. Beck, B. Hope, & A. Rosenfeld (Eds.), Human and machine vision (pp. 481–543). New York: Academic Press.

    Google Scholar 

  • Yuille, A., Fang, F., Schrater, P., & Kersten, D. (2004). Human and ideal observers for detecting image curves. Advances in Neural Information Processing Systems, 16, 1459–1466.

    Google Scholar 

  • Zhang, D., & Lu, G. (2004). Review of shape representation and description techniques. Pattern Recognition, 37, 1–19.

    Article  MATH  Google Scholar 

  • Zhong, B., & Liao, W. (2007). Direct curvature scale space: Theory and corner detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29, 508–512.

    Article  Google Scholar 

  • Zhu, S.-C. (1999). Stochastic jump-diffusion process for computing medial axes in Markov random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21, 1158–1169.

    Article  Google Scholar 

  • Zhu, S., & Yuille, A. (1996). Forms: a flexible object recognition and modelling system. International Journal of Computer Vision, 20, 187–212.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Abteilung Allgemeine Psychologie, Justus-Liebig Universität, Otto-Behagel-str. 10, F1, 35394, Giessen, Germany

    Christoph Rasche

Authors
  1. Christoph Rasche
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christoph Rasche.

Electronic Supplementary Material

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rasche, C. An Approach to the Parameterization of Structure for Fast Categorization. Int J Comput Vis 87, 337–356 (2010). https://doi.org/10.1007/s11263-009-0286-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11263-009-0286-1

Keywords

Search

Navigation