Cross-modal hash retrieval method based on supervision graph embedding

A graph embedding, cross-modal technology, applied in digital data information retrieval, instrumentation, computing and other directions, can solve the problems of reduced hash code effectiveness, unsatisfactory retrieval results, quantization errors, etc., to enhance the ability to distinguish, The effect of improving retrieval performance and improving representation ability

Inactive Publication Date: 2022-04-15
LUDONG UNIVERSITY +1
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Problems solved by technology

However, the threshold operation will bring quantization error, which usually leads to the reduction of the effectiveness of the hash code; 3) most hashing methods

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  • Cross-modal hash retrieval method based on supervision graph embedding
  • Cross-modal hash retrieval method based on supervision graph embedding
  • Cross-modal hash retrieval method based on supervision graph embedding

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[0054] Detailed description of the specific embodiments of the present invention will be described in conjunction with the accompanying drawings:

[0055] Although the present invention specifies two modalities of images and text, the algorithm is easily extended to other modalities and more than two modalities. For convenience, the present invention considers only two modes of image and text.

[0056] Such as figure 1 As shown, a cross-mode hash retrieval method embedded based on the supervision chart, includes the following steps:

[0057] 1) Step S1, climb the graphic sample pair on the webpage that can be developed from the image and text mode, build a graphic data set, and randomly divided the data set as a training set and test set;

[0058] 2) Step S2, extract 512-dimensional Gist features and the 1000-dimensional Bow feature of all images in the training set and test concentration, respectively;

[0059] 3) Step S3, design the overall target function of the cross-mode hash...

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Abstract

The invention discloses a cross-modal hash retrieval method based on supervision graph embedding, and belongs to the technical field of multimedia retrieval. Constructing an image and text modal data set, and dividing the image and text modal data set into a training set and a test set; extracting features of all image and text samples of the training set and the test set, and mapping the extracted features to a nonlinear kernel space by using a radial basis kernel function to improve the characterization capability of the features; generating a pairwise similarity matrix of the samples by utilizing the category labels, and further generating a Laplacian matrix; generating a semantic subspace by utilizing the category label; learning a mapping matrix for image and text modalities by utilizing intra-modal similarity keeping based on an image embedding method and inter-modal similarity keeping based on a semantic subspace; learning an orthogonal rotation matrix to minimize a quantization error; and an efficient iterative discrete optimization algorithm is utilized to reduce the calculation complexity of the training process.

Description

technical field [0001] The invention relates to a cross-modal hash retrieval method based on supervision graph embedding, belonging to the technical field of multimedia retrieval. Background technique [0002] In recent years, graph embedding based hashing methods have attracted much attention due to their effectiveness in cross-modal retrieval. The hash method is to map similar samples in the original space into similar hash codes, and then calculate the Hamming distance between the hash codes by XOR operation, and use the Hamming distance to measure the similarity between samples, which can significantly reduce the Computational complexity and memory overhead. Inspired by this, researchers have proposed many hashing methods for large-scale retrieval tasks in recent years. However, most methods are only applied to unimodal data, i.e. the modality type of the retrieved sample is the same as the modality type of the query sample, e.g. using images to retrieve images. But t...

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Application Information

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IPC IPC(8): G06F16/432
Inventor 姚涛张林梁李朝霞彭守永李艺茹王丽丽张淑宁
Owner LUDONG UNIVERSITY
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