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A serialized multi-feature-guided cross-media hash retrieval method and system

A cross-media, multi-feature technology, applied in the direction of unstructured text data retrieval, digital data information retrieval, text database indexing, etc., can solve the problems of underutilization, increase diversity, reduce the impact of results, increase the sticky effect

Active Publication Date: 2021-10-08
PEKING UNIV
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Problems solved by technology

[0004] However, none of the above methods make full use of the information of different scale features in the media, such as texture, object, scene information in the image, sentence and word information in the text, and the features of these images and texts at different scales contain rich complementary information, which can be used to improve the accuracy of hash retrieval

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  • A serialized multi-feature-guided cross-media hash retrieval method and system
  • A serialized multi-feature-guided cross-media hash retrieval method and system
  • A serialized multi-feature-guided cross-media hash retrieval method and system

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Embodiment Construction

[0034] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0035] The serialized multi-feature-guided cross-media hash retrieval method of the present invention, its flow is as follows figure 1 shown, including the following steps:

[0036] (1) Establish a database of images and texts, and extract features of images and texts at multiple scales.

[0037] The image feature vectors are specifically: POOL-5, FC-6, and FC-7 layer features of the VGG-19 network, which respectively represent the underlying primitive scale features, middle-level concept scale features, and high-level semantic scale features; the text features are specifically: text words The bag-of-words feature and the bag-of-words feature of each sentence are dimensionally reduced and concatenated using the principal component analysis method, representing word-scale features and sentence-scale features, respectively.

[0038] (...

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Abstract

The invention relates to a serialized multi-feature-guided cross-media hash method and system. The method includes the following steps: 1. Establishing databases of images and texts, and extracting the features of the images and texts in multiple scales respectively. 2. Input the different scale features of the image and text into the two-way cyclic neural network in the set order, and calculate the hash code of the image and text. 3. Optimize the network parameters through inter-scale association constraint functions and inter-media and intra-media hash constraint functions to realize inter-scale association mining and hash function learning. 4. In the retrieval stage, extract the different scale features of the query image or text, and generate the corresponding hash code according to the same method in step 2, so as to realize cross-media hash retrieval. The invention can excavate the association relationship between multiple scales among different media to realize the learning of the hash function, and achieve higher retrieval accuracy than the existing method.

Description

technical field [0001] The invention relates to cross-media hash retrieval between images and texts, in particular to a serialized multi-feature guided cross-media hash retrieval method and system. Background technique [0002] Cross-media retrieval is a highly flexible retrieval method, users can use any kind of media to retrieve related data of other media types. With the increase of Internet data, retrieval efficiency has gradually become an important requirement in practical applications. Cross-media hash retrieval refers to the mapping of multimedia data into a unified Hamming space, using a short Hamming code, which can not only greatly improve the speed of cross-media retrieval, but also greatly compress the required storage space, which has important applications value. [0003] Traditional text or image hash retrieval methods encounter the problem of "heterogeneous gap" when facing cross-media retrieval tasks, that is, data of different media types are distributed...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/583G06F16/31G06F40/30G06K9/62
CPCG06F40/30G06F18/22
Inventor 彭宇新叶钊达
Owner PEKING UNIV
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