Video classification method and device
A video classification and video technology, applied in the field of data processing, can solve the problem of low efficiency of classification methods, and achieve the effect of improving the accuracy rate
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Embodiment 1
[0023] Such as figure 1 As shown, the present invention provides a video classification method, comprising:
[0024] Step 101, obtain the multimodal feature vector corresponding to the video to be classified.
[0025] In this embodiment, the process of obtaining the multimodal feature vector through step 101 includes: obtaining the image features of the video to be classified; obtaining the text features of the video to be classified; fusing the image features and the text features to obtain the video corresponding to the video to be classified The multimodal feature vector of .
[0026] Among them, the way to obtain the image features of the video to be classified can be either feature extraction, or a combination of fine-tuning FineTune and feature extraction, or a classification model such as I3D / P3D / TSN network, which will not be repeated here. A repeat.
[0027] The way to obtain the text features of the video to be classified can be a modeling method such as Bag Of Wo...
Embodiment 2
[0041] Such as figure 2 As shown, the embodiment of the present invention provides a video classification device, including:
[0042] A vector acquisition unit 201, configured to acquire a multimodal feature vector corresponding to the video to be classified;
[0043] The classification unit 202 is connected with the vector acquisition unit and the pre-trained multi-level multi-label classification model, and is used to input the multi-modal feature vector into the pre-trained multi-level multi-label classification model to obtain the hierarchical classification of the video to be classified;
[0044] Pre-trained multi-level multi-label classification model 203, including:
[0045] A global probability acquisition module that obtains the global classification probability by combining a densely connected convolutional neural network with a multi-layer perceptron;
[0046] A local probability acquisition module that obtains local classification probabilities of each layer thr...
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