By Agma Juci Machado Traina, Caetano Traina Jr., Robson Leonardo Ferreira Cordeiro
ISBN-10: 3319119877
ISBN-13: 9783319119878
ISBN-10: 3319119885
ISBN-13: 9783319119885
This booklet constitutes the refereed complaints of the seventh foreign convention on Similarity seek and purposes, SISAP 2014, held in A Coruña, Spain, in October 2014. The 21 complete papers and six brief papers provided have been rigorously reviewed and chosen from forty five submissions. The papers are equipped in topical sections on enhancing Similarity seek equipment and methods; Indexing and purposes; Metrics and evaluate; New situations and ways; functions and particular Domains.
Read or Download Similarity Search and Applications: 7th International Conference, SISAP 2014, Los Cabos, Mexico, October 29-31, 2014. Proceedings PDF
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Additional resources for Similarity Search and Applications: 7th International Conference, SISAP 2014, Los Cabos, Mexico, October 29-31, 2014. Proceedings
Example text
We will derive later (Sec. 3) that this paradigm leads to standard range queries. Now, a key-frame Fi having all the centroids omitted has not properly defined the centroid distance distuvi . , the worst match. 3 Indexing the Feature Space In order to efficiently process range queries in the utilized 5-dimensional vector space (x, y, L, a, b) using the Euclidean distance, we investigate both spatial and metric indexing approaches, each represented by a suitable method. Since the utilized feature extraction does not favor any key-frame region, the distribution of the position coordinates shows high degree of uniformity and thus we have selected a grid index as the representative of spatial indexing methods.
Syst. : Optimal Aggregation Algorithms for Middleware. In: Proc. of the 20th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2001, pp. 102–113. : A Preference-Based Approach for Interactive Weight Learning: Learning Weights Within a Logic-Based Query Language. : Adapting Metric Indexes for Searching in Multi-Metric Spaces. Multimedia Tools Appl. : The M2-tree: Processing Complex Multi-Feature Queries with Just One Index. : Ranking in Spatial Databases. R. ) SSD 1995. LNCS, vol.
To build the index, metric filter refinement approaches [4, 6] compute one matrix of distances between pivots and database objects per feature. Algorithm 1 depicts the filtering phase for a kNN-query with multiple features. At first, bounds for each partial distance are computed based on the precomputed distance matrices and Equation (1) (line 3). Subsequently, these partial bounds are combined into aggregated bounds by Equation (3) (line 4). Objects having a higher aggregated lower bound lbiagg than the kth lowest aggregated upper bound ubiagg seen so far (tmax ) are excluded from the search (lines 5 and 9).
Similarity Search and Applications: 7th International Conference, SISAP 2014, Los Cabos, Mexico, October 29-31, 2014. Proceedings by Agma Juci Machado Traina, Caetano Traina Jr., Robson Leonardo Ferreira Cordeiro
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