Titlebook: Similarity Search and Applications; 6th International Co Nieves Brisaboa,Oscar Pedreira,Pavel Zezula Conference proceedings 2013 Springer-V

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Machine Learning for Image Classification and Clustering Using a Universal Distance Measurehow different the image is from a set of image prototypes. We use the recently introduced Universal Image Distance (UID) [1] to compare the similarity between an image and a prototype image. The advantage in using the UID is the fact that no domain knowledge nor any image analysis need to be done. E

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Faster Algorithms for Tree Similarity Based on Compressed Enumeration of Bounded-Sized Ordered Subtr enumeration algorithms that enumerates all subtrees of an input tree . in the form of their compressed bit signatures. For the task of enumerating all compressed bit signatures of .-subtrees in an ordered tree ., we first present an enumeration algorithm in .(.)-delay, and then, present another enu

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Principal Directions-Based Pivot Placemente been introduced in the literature. In this paper, we propose a pivot placement strategy which exploits the natural data orientation in order to select space points which achieve a good alignment with the whole data to be indexed. Comparison with existing methods substantiates the effectiveness of

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Pivot Selection Strategies for Permutation-Based Similarity Search objects are represented as appropriately generated permutations of a set of pivots (or reference objects). Similarity queries are executed by searching for data objects whose permutation representation is similar to that of the query. This, of course assumes that similar objects are represented by

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Quantized Ranking for Permutation-Based Indexinglearning and data mining. The large volume of data that typical applications should handle imposes to find approximate solutions for the similarity search problem. Permutation-based indexing is one of the most recent techniques for approximate similarity search. Objects are represented by lists orde

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Quicker Similarity Joins in Metric Spacesy, where . is a metric distance function and . ∈ ℝ. is user supplied query radius. In particular we are interested in the case where we have no index available (nor we can afford to build it) for either . or .. In this paper we improve the Quickjoin algorithm (Jacox and Samet, 2008), based on the we