Search results for "Jaro–Winkler distance"

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Learning Similarity Scores by Using a Family of Distance Functions in Multiple Feature Spaces

2017

There exist a large number of distance functions that allow one to measure similarity between feature vectors and thus can be used for ranking purposes. When multiple representations of the same object are available, distances in each representation space may be combined to produce a single similarity score. In this paper, we present a method to build such a similarity ranking out of a family of distance functions. Unlike other approaches that aim to select the best distance function for a particular context, we use several distances and combine them in a convenient way. To this end, we adopt a classical similarity learning approach and face the problem as a standard supervised machine lea…

Training setbusiness.industryFeature vectorSimilarity heuristicPattern recognition02 engineering and technologyMachine learningcomputer.software_genreSemantic similarityArtificial Intelligence020204 information systemsNormalized compression distance0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligenceJaro–Winkler distancebusinesscomputerClassifier (UML)SoftwareSimilarity learningMathematicsInternational Journal of Pattern Recognition and Artificial Intelligence
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Parallel distance transforms on pyramid machines: Theory and implementation

1990

Abstract A distance transform of a binary image is an array each of whose elements gives the distance from the corresponding pixel to the closest ‘1’ in the binary image. Distance transforms have uses in image matching and shape analysis, among other applications. We present a parallel algorithm for weighted distance transforms that runs particularly efficiently on hierarchical cellular-logic machines, a subclass of the architectures known as pyramid machines. The algorithm computes the 3–4 distance transform; however it can be readily adapted to the city-block (‘Manhattan’) and chessboard distance measures. The algorithm runs in O(M) time, for an M × M image. Since it avoids using arithmet…

business.industryBinary imageParallel algorithmImage processingDistance measuresControl and Systems EngineeringSignal ProcessingComputer visionComputer Vision and Pattern RecognitionArtificial intelligencePyramid (image processing)Jaro–Winkler distanceElectrical and Electronic EngineeringGilbert–Johnson–Keerthi distance algorithmbusinessAlgorithmDistance transformSoftwareMathematicsSignal Processing
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