333,822 research outputs found
The similarity metric
A new class of distances appropriate for measuring similarity relations
between sequences, say one type of similarity per distance, is studied. We
propose a new ``normalized information distance'', based on the noncomputable
notion of Kolmogorov complexity, and show that it is in this class and it
minorizes every computable distance in the class (that is, it is universal in
that it discovers all computable similarities). We demonstrate that it is a
metric and call it the {\em similarity metric}. This theory forms the
foundation for a new practical tool. To evidence generality and robustness we
give two distinctive applications in widely divergent areas using standard
compression programs like gzip and GenCompress. First, we compare whole
mitochondrial genomes and infer their evolutionary history. This results in a
first completely automatic computed whole mitochondrial phylogeny tree.
Secondly, we fully automatically compute the language tree of 52 different
languages.Comment: 13 pages, LaTex, 5 figures, Part of this work appeared in Proc. 14th
ACM-SIAM Symp. Discrete Algorithms, 2003. This is the final, corrected,
version to appear in IEEE Trans Inform. T
Connectionism and psychological notions of similarity
Kitcher (1996) offers a critique of connectionism based on the belief that connectionist information processing relies inherently on metric similarity relations. Metric similarity measures are independent of the order of comparison (they are symmetrical) whereas human similarity judgments are asymmetrical. We answer this challenge by describing how connectionist systems naturally produce asymmetric similarity effects. Similarity is viewed as an implicit byproduct of information processing (in particular categorization) whereas the reporting of similarity judgments is a separate and explicit meta-cognitive process. The view of similarity as a process rather than the product of an explicit comparison is discussed in relation to the spatial, feature, and structural theories of similarity
ACCDIST: A Metric for comparing speakers' accents
This paper introduces a new metric for the quantitative assessment of the similarity of speakers' accents. The ACCDIST metric is based on the correlation of inter-segment distance tables across speakers or groups. Basing the metric on segment similarity within a speaker ensures that it is sensitive to the speaker's pronunciation system rather than to his or her voice characteristics. The metric is shown to have an error rate of only 11% on the accent classification of speakers into 14 English regional accents of the British Isles, half the error rate of a metric based on spectral information directly. The metric may also be useful for cluster analysis of accent groups
Learning Deep Similarity Metric for 3D MR-TRUS Registration
Purpose: The fusion of transrectal ultrasound (TRUS) and magnetic resonance
(MR) images for guiding targeted prostate biopsy has significantly improved the
biopsy yield of aggressive cancers. A key component of MR-TRUS fusion is image
registration. However, it is very challenging to obtain a robust automatic
MR-TRUS registration due to the large appearance difference between the two
imaging modalities. The work presented in this paper aims to tackle this
problem by addressing two challenges: (i) the definition of a suitable
similarity metric and (ii) the determination of a suitable optimization
strategy.
Methods: This work proposes the use of a deep convolutional neural network to
learn a similarity metric for MR-TRUS registration. We also use a composite
optimization strategy that explores the solution space in order to search for a
suitable initialization for the second-order optimization of the learned
metric. Further, a multi-pass approach is used in order to smooth the metric
for optimization.
Results: The learned similarity metric outperforms the classical mutual
information and also the state-of-the-art MIND feature based methods. The
results indicate that the overall registration framework has a large capture
range. The proposed deep similarity metric based approach obtained a mean TRE
of 3.86mm (with an initial TRE of 16mm) for this challenging problem.
Conclusion: A similarity metric that is learned using a deep neural network
can be used to assess the quality of any given image registration and can be
used in conjunction with the aforementioned optimization framework to perform
automatic registration that is robust to poor initialization.Comment: To appear on IJCAR
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