Deep layer feature fusion-based Chinese traditional visual culture symbol identification method

A feature fusion and symbol recognition technology, applied in the field of image processing and computer vision, can solve the problems of slow convergence, missing features, useless, etc., to achieve the effect of simple system construction, improved work efficiency, and accurate results

Inactive Publication Date: 2017-06-13
COMMUNICATION UNIVERSITY OF CHINA
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Problems solved by technology

[0012] (1) In the traditional method, the final feature is sent to the last layer, that is to say, the network will filter out the information it considers useless, but the filtered information is not necessarily useless, and it is also useful for image details. Certain expressive ability, so the shortcoming is that there will be loss of characteristics
[0013] (2) The last layer of the traditional neural network is connected to Softmax, and the Softmax logistic regression model uses the gradient descent method to update the parameters. In this case, the conver

Method used

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  • Deep layer feature fusion-based Chinese traditional visual culture symbol identification method
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  • Deep layer feature fusion-based Chinese traditional visual culture symbol identification method

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Embodiment 1

[0034] Embodiment 1: as figure 1 , figure 2 , image 3 , Figure 4 , Figure 5 As shown, the Chinese traditional visual culture symbol recognition method based on deep hierarchical feature fusion includes the following steps:

[0035] Step 1: The system first obtains the symbol data of traditional Chinese visual culture, converts the obtained data into lmdb format, and then sends it to the prepared convolutional neural network for training and testing, and obtains the recognition result A at this time;

[0036] Step 2: Save the trained model as ***.caffemodel, and then extract the features of each layer from the trained model. There are 5 convolutional layers and 3 fully connected layers;

[0037] Step 3: Use the idea of ​​spatial pyramid to assign corresponding weights to the features extracted from each layer in step 2. The weights are obtained by Softmax regression. Then serially merge the features of each layer into a long vector;

[0038] Step 4: Reduce and normal...

Embodiment 2

[0059] Embodiment 2: as figure 1 , figure 2 , image 3 , Figure 4 , Figure 5 as shown,

[0060] Image classification mainly includes two processes: one is the feature extraction process, and the other is classifier design. Because the neural network (feature learning) can learn universal features from the original image, the traditional classifier has superior classification performance. It is natural to think of combining the neural network (feature learning) with the traditional classifier, so that the process of the entire pattern recognition system is fully automatic (automatic) and trainable (trainable).

[0061] The convolutional neural network can be regarded as a combination of feature extraction and classifier. From the perspective of the mapping of its various layers, it is similar to a feature extraction process, extracting features at different levels. But if the mapping goes back and forth, and finally maps to several tags, then it has the function of cla...

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Abstract

The invention discloses a deep layer feature fusion-based Chinese traditional visual culture symbol identification method, and belongs to the technical field of image processing and computer vision. The method comprises the steps of firstly training a classification model by utilizing a convolutional neural network in deep learning; secondly extracting visual culture symbol features of each layer in the trained model, calculating a weight of each layer by utilizing Softmax regression, and combining the features of each layer into a long vector, which serves as an image feature representation of each type of image; thirdly performing PCA dimension reduction and normalization on the extracted features, and inputting the features to a shallow learning SVM for performing classification; and finally by utilizing an ensemble learning thought, combining an identification result of deep learning with an identification result of deep and shallow learning combination by utilizing a regression tree to obtain a final classification result.

Description

technical field [0001] The invention relates to a recognition method of traditional Chinese visual culture symbols based on deep hierarchical feature fusion, and belongs to the technical field of image processing and computer vision. Background technique [0002] Chinese traditional visual cultural symbols are the historical and cultural heritage of the Chinese nation for thousands of years, and also a symbol of Chinese traditional culture. It condenses the wisdom and strength of the Chinese people, and becomes a way for the world to interpret China. It can represent China and influence the world. The study of Chinese traditional cultural symbols has far-reaching significance. At present, the inevitable trend of globalization has made traditional culture face a major impact. Therefore, it is objective and inevitable to re-examine and inherit Chinese traditional culture, and traditional cultural symbols as traditional culture A kind of symbol and logo, which has a far-reachi...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/2411G06F18/214
Inventor 吴晓雨杨成谭笑马禾朱贝贝杨磊
Owner COMMUNICATION UNIVERSITY OF CHINA
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