Learning colours from textures by sparse manifold embedding
Signal Processing
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Recently, databases have been used to store multimedia data such as images, maps, video clips, and music clips. In order to search them, they should be represented by various features, which are composed of high-dimensional vectors. As a result, the dimensionality of data is increased considerably, which causes ‘the curse of dimensionality’. The increase of data dimensionality causes poor performance of index structures. To overcome the problem, the research on the dimensionality reduction has been conducted. However, some reduction methods do not guarantee no false dismissal, while others incur high computational cost. This paper proposes dimensionality reduction techniques that guarantee no false dismissal while providing efficiency considerable by approximating distances with a few values. To provide the no false dismissal property, approximated distances should always be smaller than original distances. The Cauchy–Schwarz inequality and two trigonometrical equations are used as well as the dimension partitioning technique is applied to approximate distances in such a way to reduce the difference between the approximated distance and the original distance. As a result, the proposed techniques reduce the candidate set of a query result for efficient query processing.