Abstract
The paper proposes multi-distinctive MSER features (md-MSER) which are MSER keypoints combined with a number of encompassed keypoints of another type, which should also be affine-invariants (e.g. Harris-Affine keypoints) to maintain the invariance of the proposed method. Such a bundle of keypoints is jointly represented by the corresponding number of SIFT-based descriptors which characterize both visual and spatial properties of md-MSERs. Therefore, matches between individual md-MSER features indicate both visual and configurational similarities so that true feature correspondences can be established (at least in some applications) without the verification of spatial consistencies (i.e. the computational costs of detecting contents visually similar in a wider context are significantly reduced). The paper briefly presents the principles of building and representing md-MSER features. Then, performances of md-MSER-based algorithms are experimentally evaluated in two benchmark scenarios of image matching and retrieval. In particular, md-MSER algorithms are compared to typical alternatives based on other affine-invariant keypoints.
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Śluzek, A. (2015). Multi-distinctive MSER Features and Their Descriptors: A Low-Complexity Tool for Image Matching. In: Battiato, S., Blanc-Talon, J., Gallo, G., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2015. Lecture Notes in Computer Science(), vol 9386. Springer, Cham. https://doi.org/10.1007/978-3-319-25903-1_58
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DOI: https://doi.org/10.1007/978-3-319-25903-1_58
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