A compact deep CNN feature indexing method based on SIFT embeddings
A feature indexing and depth technology, applied in the field of image retrieval in multimedia technology, can solve the problems of low sparsity, low indexing efficiency, ignoring the underlying visual characteristics, etc., and achieve the effect of improving sparsity and retrieval accuracy.
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[0047] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.
[0048] The present invention provides a compact deep CNN feature indexing method based on SIFT embedding. The method is aimed at deep CNN features. In order to improve the sparsity, an energy-based coefficient selection method is defined; and then each dimension in the deep CNN feature is regarded as Make a visual word (Visual Word) and build an inverted list, that is, for each picture, if its eigenvalue in the corresponding dimension is not zero, t...
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