Sparse dimension reduction-based spectral hash indexing method

A hash index, hash technology, used in special data processing applications, instruments, electrical digital data processing and other directions

Inactive Publication Date: 2010-11-24
ZHEJIANG UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

Although PCA is a commonly used data dimensionality reduction method, the principal components obtained by PCA are linear combinations of almost a

Method used

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  • Sparse dimension reduction-based spectral hash indexing method
  • Sparse dimension reduction-based spectral hash indexing method
  • Sparse dimension reduction-based spectral hash indexing method

Examples

Experimental program
Comparison scheme
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Embodiment

[0079] Example: We implemented the above method on the TREC-V2009 dataset. Randomly select 3047 images in all key frames as the original image, use the variable degree Gaussian function to calculate each pixel, get the key pixel, extract the underlying features of the image, and then use the k-means method to express it as a visual word in 300-dimensional space , thus forming the original image feature matrix X. Then the Hamming space vector is obtained through the following method steps.

[0080] Input: original training image set X and encoding length m;

[0081] Output: binary encoding of each image;

[0082] Method description:

[0083] Step 1: Calculate the covariance matrix Σ of X;

[0084] Step 2: Calculate the m sparse principal components of ∑ to obtain the matrix M;

[0085] Step 3: B=X×M;

[0086] Step 4: Calculate N×m of the matrix ∑ and in ascending order;

[0087] Step 5: Map B to Hamming space.

[0088] In addition, 45 images are randomly selected as t...

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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]...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06K9/4671G06V10/462
Inventor 吴飞张啸邵健
Owner ZHEJIANG UNIV
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