Abstract
In this paper we evaluate the performance of the two most successful state-of-the-art descriptors, applied to the task of visual object detection and localization in images. In the first experiment we use these descriptors, combined with binary classifiers, to test the presence/absence of object in a target image. In the second experiment, we try to locate faces in images, by using a structural model. The results show that HMAX performs slightly better than SIFT in these tasks.
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Moreno, P., Marín-Jiménez, M.J., Bernardino, A., Santos-Victor, J., de la Blanca, N.P. (2007). A Comparative Study of Local Descriptors for Object Category Recognition: SIFT vs HMAX. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72847-4_66
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DOI: https://doi.org/10.1007/978-3-540-72847-4_66
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72846-7
Online ISBN: 978-3-540-72847-4
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