Abstract
In this paper we describe a novel technique for detecting salient regions in an image. The detector is a generalization to affine invariance of the method introduced by Kadir and Brady [10]. The detector deems a region salient if it exhibits unpredictability in both its attributes and its spatial scale.
The detector has significantly different properties to operators based on kernel convolution, and we examine three aspects of its behaviour: invariance to viewpoint change; insensitivity to image perturbations; and repeatability under intra-class variation. Previous work has, on the whole, concentrated on viewpoint invariance. A second contribution of this paper is to propose a performance test for evaluating the two other aspects.
We compare the performance of the saliency detector to other standard detectors including an affine invariance interest point detector. It is demonstrated that the saliency detector has comparable viewpoint invariance performance, but superior insensitivity to perturbations and intra-class variation performance for images of certain object classes.
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Agarwal, S., Roth, D.: Learning a sparse representation for object detection. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 113–130. Springer, Heidelberg (2002)
Baumberg, A.: Reliable feature matching across widely separated views. In: Proc. Computer Vision Pattern Recognition, pp. 774–781 (2000)
Borenstein, E., Ullman, S.: Class-specific, top-down segmentation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 109–124. Springer, Heidelberg (2002)
Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: Proc. Computer Vision Pattern Recognition, pp. II: 264–271 (2003)
Foley, J.A., Van Dam, A.: Fundamentals of Interactive Computer Graphics. Addison-Wesley, Reading (1982)
Griffin, L.D.: Scale-imprecision space. Image and Vision Computing 15, 369–398 (1997)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Proc. Alvey Vision Conf., Manchester, pp. 189–192 (1988)
Kadir, T.: Scale, Saliency and Scene Description. PhD thesis, University of Oxford (2002)
Kadir, T., Boukerroui, D., Brady, J.M.: An analysis of the scale saliency algorithm. Technical Report OUEL No: 2264/03, University of Oxford (2003)
Kadir, T., Brady, J.M.: Scale, saliency and image description. Intl. J. of Computer Vision 45(2), 83–105 (2001)
Koenderink, J.J., van Doorn, A.J.: Representation of local geometry in the visual system. Biological Cybernetics 63, 291–297 (1987)
Koenderink, J.J., van Doorn, A.J.: The structure of locally orderless images. Intl. J. of Computer Vision 31(2/3), 159–168 (1999)
Kok-Wiles, S., Brady, M., Highnam, R.: Comparing mammogram pairs for the detection of lesions. In: Proc. Intl. Workshop on Digital Mammography, pp. 103–110 (1998)
Lindeberg, T.: Detecting salient blob-like image structures and their scales with a scale-space primal sketch: A method for focus-of-attention. Intl. J. of Computer Vision 11(3), 283–318 (1993)
Lindeberg, T., ter Haar Romeny, B.M.: Linear scale-space: I. basic theory, II. early visual operations. In: ter Haar Romeny, B.M. (ed.) Geometry-Driven Diffusion, Kluwer Academic Publishers, Dordrecht (1994)
Lowe, D.G.: Object recognition from local scale-invariant features. In: Proc. Intl. Conf. on Computer Vision, pp. 1150–1157 (1999)
Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: Proc. British Machine Vision Conf., pp. 384–393 (2002)
Mikolajczyk, K., Schmid, C.: Indexing based on scale invariant interest points. In: Proc. Intl. Conf. on Computer Vision (2001)
Mikolajczyk, K., Schmid, C.: An affine invariant interest point detector. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 128–142. Springer, Heidelberg (2002)
Schaffalitzky, F., Zisserman, A.: Multi-view matching for unordered image sets, or “How do I organize my holiday snaps?”. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 414–431. Springer, Heidelberg (2002)
Schmid, C., Mohr, R.: Local greyvalue invariants for image retrieval. IEEE Trans. Pattern Analysis and Machine Intelligence 19(5), 530–535 (1997)
Tuytelaars, T., Van Gool, L.: Wide baseline stereo based on local, affinely invariant regions. In: Proc. British Machine Vision Conf., pp. 412–422 (2000)
Weber, M., Welling, M., Perona, P.: Unsupervised learning of models for recognition. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 18–32. Springer, Heidelberg (2000)
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Kadir, T., Zisserman, A., Brady, M. (2004). An Affine Invariant Salient Region Detector. In: Pajdla, T., Matas, J. (eds) Computer Vision - ECCV 2004. ECCV 2004. Lecture Notes in Computer Science, vol 3021. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24670-1_18
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