Selecting Good Keys for Triangle-Inequality-Based Pruning Algorithms
نویسندگان
چکیده
A new class of algorithms based on the triangle inequality has recently been proposed for use in contentbased image retrieval. These algorithms rely on comparing a set of key images to the database images, and storing the computed distances. Query images are later compared to the keys, and the triangle inequality is used to speedily compute lower bounds on the distance from the query to each of the database images. This paper addresses the question of increasing performance of this algorithm by the selection of appropriate key images. Several algorithms for key selection are proposed and tested.
منابع مشابه
On Tighter Inequalities for Efficient Similarity Search in Metric Spaces
Similarity search consists of the efficient retrieval of relevant information satisfying user formulated query conditions from a database with prebuilt indexing structures. Since the evaluation of the distance functions between queries and indexed objects is often computationally expensive, there have been many attempts to build indexing structures that use as few distance computations as possi...
متن کاملApproximation Guarantees for Max Sum and Max Min Facility Dispersion with Parameterised Triangle Inequality and Applications in Result Diversification
Facility Dispersion Problem, originally studied in Operations Research, has recently found important new applications in Result Diversification approach in information sciences. This optimisation problem consists in selecting a small set of p items out of a large set of candidates to maximise a given objective function. The function expresses the notion of dispersion of a set of selected items ...
متن کاملPruning and Model-selecting Algorithms in the Rbf Frameworks Constructed by Support Vector Learning
This paper presents the pruning and model-selecting algorithms to the support vector learning for sample classification and function regression. When constructing RBF network by support vector learning we occasionally obtain redundant support vectors which do not significantly affect the final classification and function approximation results. The pruning algorithms primarily based on the sensi...
متن کاملPivot-based Metric Indexing
The general notion of a metric space encompasses a diverse range of data types and accompanying similarity measures. Hence, metric search plays an important role in a wide range of settings, including multimedia retrieval, data mining, and data integration. With the aim of accelerating metric search, a collection of pivotbased indexing techniques for metric data has been proposed, which reduces...
متن کاملTOP: A Compiler-Based Framework for Optimizing Machine Learning Algorithms through Generalized Triangle Inequality
This paper describes our recent research progress on generalizing triangle inequality (TI) to optimize Machine Learning algorithms that involve either vector dot products (e.g., Neural Networks) or distance calculations (e.g., KNN, KMeans). The progress includes a new form of TI named Angular Triangular Inequality, abstractions to enable unified treatment to various ML algorithms, and TOP, a co...
متن کامل