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Cross-modal retrieval method and device based on adversarial projection learning hash

A cross-modal, projection technology, applied in the field of computer vision, can solve problems such as huge quantization error, optimization instability, etc., to achieve the effect of improving accuracy, reducing heterogeneous gap, and strengthening semantic learning ability

Inactive Publication Date: 2021-01-12
ZHEJIANG UNIV OF TECH
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Problems solved by technology

Under such loose conditions, it will lead to huge quantization errors, optimization instability and other problems.

Method used

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  • Cross-modal retrieval method and device based on adversarial projection learning hash
  • Cross-modal retrieval method and device based on adversarial projection learning hash
  • Cross-modal retrieval method and device based on adversarial projection learning hash

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Embodiment Construction

[0030] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, not to limit the present application.

[0031] Hash learning maps data into binary strings through machine learning mechanisms, which can significantly reduce data storage and communication overhead, thereby effectively improving the efficiency of the learning system. The purpose of hash learning is to learn the binary hash code representation of the data, so that the hash code retains the neighbor relationship in the original space as much as possible, that is, maintains the similarity. Specifically, each data point will be represented as a compact binary string code (hash code), and two similar points in the original space sho...

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Abstract

The invention discloses a cross-modal retrieval method and device based on adversarial projection learning hash, and the method comprises the steps of carrying out the retrieval through employing a trained neural network which comprises an image network and a text network, and the image network and the text network respectively comprise a feature function layer, a hash function layer and a symbolfunction layer; inputting to-be-retrieved image data or text data into respective corresponding feature function layers to obtain image features or text features, and inputting the extracted image features or text features into respective corresponding hash function layers; inputting the output of the hash function layer into the corresponding symbol function layer to obtain a final hash code; andcomparing the obtained hash code with a hash code of a text or an image in a database to obtain a query result. The retrieval method disclosed by the invention is relatively high in precision and relatively high in retrieval efficiency.

Description

technical field [0001] The invention relates to the technical field of image big data processing and analysis in the field of computer vision and natural language processing and analysis, in particular to a cross-modal retrieval method and device based on adversarial projection learning hashing. Background technique [0002] With the development of modern network technology, a large amount of multimodal data is generated in people's daily life every day, including text, audio, video and image. Meanwhile, efficient retrieval from such a large amount of multimodal data has become a great challenge, among which image-to-text and text-to-image retrieval are the most widely studied. Retrieval based on hash learning is widely used in various retrieval tasks due to its high efficiency and convenient storage. Hash learning learns the optimal hash function, and maps high-dimensional data into binary codes on the premise of ensuring the similarity between the data in the original spa...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/31G06F16/33G06F16/35G06F40/30G06K9/62G06N3/04
CPCG06F16/325G06F16/334G06F40/30G06F16/35G06N3/045G06F18/214
Inventor 白琮曾超马青陈胜勇
Owner ZHEJIANG UNIV OF TECH
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