Sparse dimension reduction-based spectral hash indexing method

Abstract

The invention discloses a sparse dimension reduction-based spectral hash indexing method, which comprises the following steps: 1) extracting image low-level features of an original image by using an SIFT method; 2) clustering the image low-level features by using a K-means method, and using each cluster center as a sight word; 3) reducing the dimensions of the vectors the sight words by using a sparse component analysis method directly and making the vectors sparse; 4) resolving an Euclidean-to-Hamming space mapping function by using the characteristic equation and characteristic roots of a weighted Laplace-Beltrami operator so as to obtain a low-dimension Hamming space vector; and 5) for an image to be searched, the Hamming distance between the image to be searched and the original image in the low-dimensional Hamming space and using the Hamming distance as the image similarity computation result. In the invention, the sparse dimension reduction mode instead of a spectral has principle component analysis dimension reduction mode is adopted, so the interpretability of the result is improved; and the searching problem of the Euclidean space is mapped into the Hamming space, and the search efficiency is improved.

Description

technical field

[0001] The invention relates to an image search method, in particular to a spectral hash index method based on sparse dimensionality reduction. Background technique

[0002] With the continuous development of the Internet, the index mechanism in the traditional image search method has been difficult to meet the high-level needs of users, and the exponential growth of massive data has brought great challenges to improving the efficiency of search engines.

[0003] At present, massive image data presents high-dimensional and multi-level characteristics. For a given Internet image data, the extracted visual features are often hundreds or even thousands. These high-dimensional data bring many difficulties to image similarity calculation and semantic analysis.

[0004] In order to improve the efficiency of high-dimensional image data processing, the following three methods have been widely studied and become international and domestic academic hotspots:

[0005] ...

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