Abstract
In this work, we study local feature extraction methods and evaluate their performance in detecting local features from the salient regions of images. In order to measure the detectors’ performance, we compared the detected regions to gaze fixations obtained from the eye movement recordings of human participants viewing two types of images: natural images (photographs) and abstract/surreal images. The results indicate that all of the six evaluated local feature detectors perform clearly above chance level. The Hessian-Affine detector performs the best and almost reaches the performance level of state-of-the-art saliency detection methods.
Chapter PDF
Similar content being viewed by others
References
Akshat, D., Rachit, D., Bernard, G.: Do Humans Fixate on Interest Points? In: Proc. of International Conference on Pattern Recognition. Tsukuba Science City, Japan (2012)
Bay, H., Tuytelaars, T., Van Gool, L.: Surf: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)
Biederman, I.: Recognition-by-components: A theory of human image understanding. Psychological Review 94(2), 115–147 (1987)
Borji, A., Itti, L.: State-of-the-art in visual attention modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(1), 185–207 (2013)
Chum, O., Mikulik, A., Perdoch, M., Matas, J.: Total recall ii: Query expansion revisited. In: Proc. of Computer Vision and Pattern Recognition, pp. 889–896 (2011)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes (VOC) challenge. International Journal of Computer Vision 88(2), 303–338 (2010)
Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: Proc. of Computer Vision and Pattern Recognition (2008)
Harding, P., Robertson, N.: A comparison of feature detectors with passive and task-based visual saliency. In: Salberg, A.-B., Hardeberg, J.Y., Jenssen, R. (eds.) SCIA 2009. LNCS, vol. 5575, pp. 716–725. Springer, Heidelberg (2009)
Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Proc. of Neural Information Processing Systems, pp. 545–552. MIT Press (2007)
Itti, L., Koch, C.: Computational modelling of visual attention. Nature Reviews Neuroscience 2(3), 194–203 (2001)
Judd, T., Ehinger, K., Durand, F., Torralba, A.: Learning to predict where humans look. In: Proc. of International Conference on Computer Vision (2009)
Judd, T., Durand, F.D., Torralba, A.: A Benchmark of Computational Models of Saliency to Predict Human Fixations. Tech. rep. (2012)
Laine-Hernandez, M., Kinnunen, T., Kamarainen, J.K., Lensu, L., Kälviäinen, H., Oittinen, P.: Visual Saliency and Categorisation of Abstract Images. In: Proc. of International Conference on Pattern Recognition. Tsukuba Science City, Japan (2012)
Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 20, 91–110 (2004)
Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. In: Proc. of British Machine Vision Conference, pp. 384–393 (2002)
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.V.: A comparison of affine region detectors. International Journal of Computer Vision 65(1/2), 43–72 (2005)
Mikolajczyk, K., Schmid, C.: Indexing based on scale invariant interest points. In: Proc. of International Conference on Computer Vision, pp. 525–531 (2001)
Mikolajczyk, K., Schmid, C.: An affine invariant interest point detector. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part I. LNCS, vol. 2350, pp. 128–142. Springer, Heidelberg (2002)
Mikolajczyk, K., Schmid, C.: Scale & affine invariant interest point detectors. International Journal of Computer Vision 60, 63–86 (2004)
Viola, P., Jones, M.: Robust real time object detection. International Journal of Computer Vision (2001)
Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: A comprehensive study. International Journal of Computer Vision 73(2) (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kinnunen, T., Laine-Hernandez, M., Oittinen, P. (2013). Evaluating Local Feature Detectors in Salient Region Detection. In: Kämäräinen, JK., Koskela, M. (eds) Image Analysis. SCIA 2013. Lecture Notes in Computer Science, vol 7944. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38886-6_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-38886-6_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38885-9
Online ISBN: 978-3-642-38886-6
eBook Packages: Computer ScienceComputer Science (R0)