Supervision cross-modal Hash search method based on nonparametric Bayesian model

Abstract

The invention provides a supervision cross-modal Hash search method based on a nonparametric Bayesian model, used for solving the technical problem that in the existing cross-modal Hash search method, the search accuracy is low. The method comprises the implementation steps of operating to acquire normalized training data and test data; classifying the normalized training data; operating to acquire three training data parameters of the normalized training data; operating to acquire the probability that normalized image training data and normalized text training data belong to the same class; operating to acquire the posteriori probability of the training data; operating to acquire a unified Hash code of the normalized image training data and the normalized text training data; operating to acquire the Hash code of the test data; operating to acquire a Hamming distance matrix of the Hash code of the test data and the unified Hash code of the normalized image training data and the normalized text training data; and operating to acquire a search result of the test data. The method provided by the invention is high in search accuracy, and can be applied to mutual search service for images and texts of mobile terminal equipment and the Internet of things.

Description

technical field [0001] The invention belongs to the field of computer vision and pattern recognition, and relates to mutual retrieval of images and texts, in particular to a supervised cross-modal hash retrieval method based on a non-parametric Bayesian model, which can be used for image and text retrieval of mobile terminal equipment and the Internet of Things Text mutual search service. Background technique [0002] In recent years, with the rapid development of social economy and the continuous progress of science and technology, multimedia data has become the main information carrier on the Internet. These data are showing explosive growth. At this stage, big data is changing people's work and life, and it has also had a great impact on scientific research in academia. How to use these big data, how to efficiently store and manage it has become our most concerned issue. Hash-based nearest neighbor search is an effective technical means to solve large-scale multimedia d...

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Problems solved by technology

[0005] The purpose of the present invention is to address the deficiencies in the prior art above, and propose a supervised cross-modal hash retrieval method based on a non-parametric Bayesian model, which is used to solve the problems existing in the existing supervised cross-modal hash retrieval method. Technical issues with low retrieval accuracy

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