Abstract
An image descriptor based on the orientation of contrasts is proposed to facilitate image understanding. A mathematical function that satisfies the heat equation is proposed to model the parameters of an ideal edge, including the width, contrast, offset, position, and orientation. A simple method can compute the contrast and width of canny edge features. Inspired by the histogram of oriented gradients method, an image representation is proposed based on a histogram of contrasts. The proposed method encodes the weighted orientation in a sub-window. The weighting is based on the contrast instead of the gradient magnitude. Our experiments showed that the new representation was more robust against noise compared with several state-of-the-art methods. Several sub-window resolutions were also considered, which resulted in a pyramid that incorporated the global and local context. A method based on Bayes’ theorem and empirical cumulative distribution function is proposed to predict the probability of correctness for the nearest neighbor classification. The annotation and class label of an image were retrieved using a nearest neighbor method or a support vector machine classifier. This method was tested using several benchmark datasets and toolboxes, which showed that it is suitable for image understanding tasks.
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References
Kunt, M.: Digital Signal Processing Ch. 6. Artech House, Norwood (1986)
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)
Marr, D., Hildreth, E.: Theory of edge detection. Proc. R. Soc. Lond. Ser. B Biol. Sci. 207(1167), 187–217 (1980)
Hussein, A., Yang, X.: Colorization using edge-preserving smoothing filter. Signal, Image Video Process. 8(8), 1681–1689 (2012)
Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)
Poggio, T., Voorhees, H., et al.: A regularized solution to edge detection. J. Complex. 4(2), 106–123 (1988)
Elder, J.H.: Are edges incomplete. Int. J. Comput. Vis. 34(2–3), 97–122 (1999)
Zhang, X., Liu, C.: An ideal image edge detection scheme. Multidimens. Syst. Signal Process. 25(4), 659–681 (2013)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of IEEE Conference Computer Vision and Pattern Recognition, pp. 886–893 (2005)
Lazebnik, S., Schmid, C. et al.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2169–2178 (2006)
Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Bay, H., Tuytelaars, T., et al.: Surf: speeded up robust features. In: European Conference on Computer Vision, pp. 404–417 (2006)
Bosch, A., Zisserman, A., et al.: Representing shape with a spatial pyramid kernel. In: Proceedings of the 6th ACM International Conference on Image and Video Retrieval, pp. 401–408 (2007)
Vedaldi, A., Fulkerson, B.: VLFeat: An Open and Portable Library of Computer Vision Algorithms (2008)
Everingham, M., Van Gool, L., et al.: The pascal visual object classes (voc) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)
Zhang, S., Huang, J., et al.: Automatic image annotation using group sparsity. In: Proceedings of IEEE Conference of Computer Vision and Pattern Recognition, pp. 3312–3319 (2010)
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Zhang, X., Liu, C. Image understanding based on histogram of contrast. SIViP 10, 103–112 (2016). https://doi.org/10.1007/s11760-014-0707-7
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DOI: https://doi.org/10.1007/s11760-014-0707-7