Construction method of dynamic k-nearest neighbor graph and rapid image retrieval method based on dynamic k-nearest neighbor graph
A technology of k-nearest neighbor graph and construction method, applied in the field of information retrieval, can solve the problem of inability to construct high-quality k-nearest neighbor graph, achieve the effect of fast image retrieval and improve retrieval efficiency
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[0035] The invention discloses a method for constructing a dynamic k-nearest neighbor graph, which comprises the following steps:
[0036] Step 1. When a new image is added to the image data set, feature extraction is performed on the newly added image to obtain a feature vector q, and the feature vector q is added to the image feature set S; the image feature set S is in the d-dimensional space A collection of vectors obtained by feature extraction from images in the image dataset. In this embodiment, SIFT / SURF or methods such as deep learning are used for feature extraction.
[0037] Step 2. When the scale of the image feature set S is small, that is, when the number of feature vectors in the image feature set S is less than M, the distance between the vector q and all the vectors in the image feature set S is calculated by exhaustive comparison, to The calculated distance updates the k-nearest neighbor graph G. The distance here can be Euclidean distance, Hamming distance...
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