Unbalanced trademark retrieval method based on deep hash of aggregation similarity

Abstract

The invention discloses an unbalanced trademark retrieval method based on deep hash of aggregation similarity, which comprises the following steps of: 1, constructing a network architecture of a neural network, and initializing neural network parameters; 2, generating a trademark hash cluster center through good attributes of a Hadamard matrix and Bern random sampling; thirdly, constructing a trademark semantic hash cluster center based on the trademark hash cluster center; 4, for training samples of the trademark data set training set, obtaining a training set Hash coding set through a sparse Hash coding module; and 5, calculating argminLT loss and executing back propagation to optimize neural network parameters until the model converges, and generating a deep hash neural network based on aggregation similarity. Compared with the prior art, the method has the advantages that the advantages of the deep learning technology and the Hash technology are combined, similar trademarks are made to be close, different trademarks are made to be far away, therefore, the trademark training characterization capacity is improved, and the retrieval problem caused by imbalance between data classes is solved.

Description

technical field [0001] The invention relates to the field of computer technology, and more specifically, to an unbalanced trademark retrieval method based on deep hashing of aggregation similarity. Background technique [0002] As an important intellectual property right, trademark plays a pivotal role in social and economic development. How to quickly and effectively retrieve similar trademarks from a large number of trademarks will be very helpful to trademark examiners and trademark owner applicants. Therefore, it is of great practical significance to study accurate and fast trademark retrieval methods for huge trademark databases. [0003] Due to the late start of digitalization of the official patent database, the included trademark images are not complete. Therefore, like other datasets, official trademark databases suffer from data imbalance. If the training data is unbalanced, most existing learning algorithms will have a learning bias for the majority class, resu...

AI Technical Summary

Problems solved by technology

[0006] In order to solve the problems of unbalanced data and low accuracy of retrieval results in existing trademark retrieval methods in the background technology, the present invention provides an unbalanced trademark retrieval method based on deep hashing of aggregation similarity

This method combines the advantages of deep learning technology and hashing technology, adding the built-in aggregation similarity mechanism and sparse hash coding module after training, so that similar trademarks are close and different kinds of trademarks are far away, thereby improving the representation ability of trademark training and solving the problem. The retrieval problem caused by the imbalance between data classes is solved

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