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Product Quantization (PQ)

2022-08-09 10:47:00 【qq_26391203】

  1. How product quantization is used in image retrieval:

    "' After quantitative learning, for a given query sample, the query sample and library can be calculated by looking up a tableAsymmetric distance of the samples in"'

  2. A brief description of product quantization: The typical representative of vector quantization methods is the product quantization (PQ, Product
    Quantization) method, which decomposes the feature space into Cartesian products of multiple low-dimensional subspaces, and then quantize each subspace individually.In the training phase, each subspace is clustered to obtain kk centroids (ie quantizers), and the Cartesian product of all these centroids constitutes a dense division of the whole space, and can ensure that the quantization error is relatively small;After quantitative learning, for a given query sample, the asymmetric distance between the query sample and the sample in the library can be calculated by looking up the table.Approximate Nearest Neighbor Search

  3. K-means clustering algorithm: Clustering belongs to unsupervised learning, the previous regression, Naive Bayes, SVM, etc. all have the category label y, that is to say, the classification of the sample has been given in the sample.However, there is no y given in the clustered samples, only the feature x. For example, it is assumed that the stars in the universe can be represented as the point set clip_image002 [10] in the three-dimensional space.The purpose of clustering is to find the latent class y of each sample x and put together samples x of the same class y.For example, for the stars above, after clustering, the result is a cluster of stars. The points in the cluster are relatively close to each other, and the distance between the stars in the cluster is relatively far.
  4. Product quantization process idea: https://www.cnblogs.com/mafuqiang/p/7161592.html
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