Speeding up the similarity search in high-dimensional image database by multiscale filtering and dynamic programming

Cheng, Stone; Wu, Tsung-Liang

doi:10.1016/j.imavis.2006.01.014

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…Related work that utilize features in indexing, such as color or texture, include the QBIC project (Flickner et al, 1995), the previously mentioned image search engines, the Virage search engine (Bach et al, 1996), Cheng and Wu's method (Cheng and Wu, 2006) as well as Jain and Vailaya's method (Jain and Vailaya, 1996). Our method differs from these as we discretize, quantize and partition the data to provide fast indexing and searching.…”

Section: Image Indexingmentioning

confidence: 99%

HyperSAX: Fast Approximate Search of Multidimensional Data

Gydesen

Haxholm

Poulsen

et al. 2015

Proceedings of the International Conference on Pattern Recognition Applications and Methods

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The increasing amount and size of data makes indexing and searching more difficult. It is especially challenging for multidimensional data such as images, videos, etc. In this paper we introduce a new indexable symbolic data representation that allows us to efficiently index and retrieve from a large amount of data that may appear in multiple dimensions. We use an approximate lower bounding distance measure to compute the distance between multidimensional arrays, which allows us to perform fast similarity searches. We present two search methods, exact and approximate, which can quickly retrieve data using our representation. Our approach is very general and works for many types of multidimensional data, including different types of image representations. Even for millions of multidimensional arrays, the approximate search will find a result in a few milliseconds, and will in many cases return a result similar to the best match.

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