Cross-modal image audio retrieval method based on deep heterogeneous correlation learning
A cross-modal and audio technology, applied in the field of cross-modal image and audio retrieval based on deep heterogeneous correlation learning, can solve a large amount of storage space, insufficient utilization of heterogeneous correlation relations, inability to effectively select cross-modal paired samples, etc. problem, to achieve the effect of improving retrieval accuracy and reducing quantization error
Pending Publication Date: 2021-09-03
WUHAN UNIV OF TECH
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The invention discloses a cross-modal image audio retrieval method based on deep heterogeneous correlation learning. The method mainly solves the problem that heterogeneous correlation information of images and audios is not sufficiently utilized in an existing method. According to the invention, firstly, a new cross-modal pairwise construction strategy is designed to select effective image and audio pairs, which is beneficial to capturing heterogeneous correlation between images and audios. According to the invention, the relationship between the image and the audio is established by utilizing the heterogeneous correlation of the depth features, the Hash code is generated by bridging the deep feature correlation between the image and the audio for image and audio retrieval, and the quantization error between the Hash-like code and the Hash code is reduced by using the regularization constraint. According to the invention, the heterogeneous correlation of the depth features is fully utilized, and the retrieval performance is further improved.
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