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A teaching video image knowledge point dimension reduction analysis method

A dimensionality reduction analysis and teaching video technology, applied in the field of computer vision and image recognition, can solve the problems of low image retrieval accuracy and long time consumption, and achieve the effect of improving retrieval efficiency, strong learning ability and reducing loss.

Active Publication Date: 2019-05-03
信阳航空职业学院
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Problems solved by technology

[0006] In view of this, the present invention provides a method for dimensionality reduction analysis of teaching video image knowledge points, which solves the problems of low image retrieval accuracy and long time consumption in the current technology

Method used

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  • A teaching video image knowledge point dimension reduction analysis method
  • A teaching video image knowledge point dimension reduction analysis method
  • A teaching video image knowledge point dimension reduction analysis method

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Embodiment approach

[0056] In this embodiment, the sparse density map of the teaching video image knowledge points processed by Matlab is retrieved, and the Top 6 is retrieved, such as image 3 shown.

[0057] Such as Figure 4 As shown, it can be seen from the data in the table that the retrieval effect of the present invention is greatly improved compared with the traditional LeNet-5 image retrieval method, and the retrieval performance is also better than the original convolutional neural network model method CNN. Its retrieval performance is improved by 8% to 27.3% compared with traditional methods.

[0058] Such as Figure 5 Figure 6 As shown, the retrieval performance of a method for dimensionality reduction analysis of teaching video image knowledge points, according to the comparison curve of recall rate and average precision rate obtained from the first 100 or 50 most similar teaching images, from Figure 5 It can be seen that as the number of returned images increases, the recall r...

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Abstract

The invention provides a teaching video image knowledge point dimension reduction analysis method comprising the following steps: S1, collecting image data, carrying out normalization preprocessing, and establishing a teaching image database; S2, constructing an improved convolutional neural network model, and performing pre-training and parameter adjustment; S3, collecting and preprocessing the teaching image to be retrieved; S4, carrying out feature vector extraction on the teaching image established in the step S1 and the teaching image to be retrieved in the step S3 by using the model established in the step S2 to obtain an image feature library to be retrieved; S5, respectively carrying out dimension reduction processing on the high-dimensional feature vectors extracted in the step S4by using a principal component analysis method; S6, performing similarity measurement on the feature vector of the to-be-retrieved image and each feature vector in the image in the image database, returning a feature index with higher similarity, and finding out a relative image from the image library to obtain a front Topk image to obtain a retrieval result; the image retrieval precision is improved, and the problems that in the prior art, the image retrieval accuracy is low, and consumed time is long are solved.

Description

technical field [0001] The invention relates to the technical field of computer vision and image recognition, in particular to a dimensionality reduction analysis method for teaching video image knowledge points. Background technique [0002] With the rapid development of Internet technology and multimedia technology, online teaching video has gradually become an important learning method that effectively supplements classroom teaching. With the rapid increase of user groups and the increasingly diverse technical requirements, the image retrieval and analysis of teaching videos has become one of the important research contents. In order to better realize the precise query of online teaching videos and meet people's needs, researchers have proposed effective solutions, which can be summarized as content-based image retrieval and text-based image retrieval. Because the text-based image retrieval appeared earlier and the technology is not mature enough, it can't complete the i...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/73G06N3/04G06N3/08
Inventor 刘道华崔玉爽齐泓深祁传达曾召霞赵岩松宋玉婷
Owner 信阳航空职业学院
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