A Wavelet-based Approach to Image Feature Stability Assessment
Authors: Antonio Robles-Kelly and Roland Göcke
Presented by Roland Göcke at the Beyond Patches Workshop of the
IEEE Computer Society Conference on Computer Vision and Pattern
Recognition CVPR2006, New York, USA, 17-22 June 2006
Abstract
In this paper, we present a novel method for assessing image-feature
stability. The method hinges on applying the discrete wavelet transform to
the image features under study throughout a number of video frames in an
image sequence. For purposes of stability assessment, we recover the
image-feature vectors for each video frame and then track them trough a
series of consecutive frames in the image sequence. We apply the discrete
wavelet transform to the time series constructed from the pairwise
Euclidean distances for each of the image features under study and use the
wavelet transform coefficients to assess their stability. We then recover
the stable features by clustering together those time series which exhibit
largely constant low-pass wavelet coefficients. We present results of the
stability analysis for Harris corners, Maximally Stable Extremal Regions,
and Scale Invariant Feature Transform regions extracted from two
real-world video sequences. We also elaborate on the applications of our
method to indexing, retrieval, and compression of stable image feature
vectors.
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Last modified: Thu Jul 06 10:32:11 AUS Eastern Standard Time 2006