Method and device for image classification
An image and image representation technology, applied in the field of computer vision, can solve the problems that affect the accuracy of image classification, high-level semantic features of differential information cannot be expressed, etc., to achieve the effect of strong ease of use and practicability, and improved accuracy
Active Publication Date: 2019-05-07
SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
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The high-level semantic features of the difference information on these objects are often unable to express, which affects the accuracy of image classification
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[0019] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the invention may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
[0020] In order to illustrate the technical solutions of the present invention, specific examples are used below to illustrate.
[0021] see figure 1 , figure 1 The implementation flow of the image classification method provided by the embodiment of the present invention is shown, and the method is applicable to various terminal devices, such as personal computers, tablet computers, mobile phone...
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The invention is applicable to the technical field of computer vision and provides an image classification method and device. The method includes: extracting the features of the intermediate convolutional layer and the features of the fully connected layer of the input image through a deep convolutional neural network; preprocessing the extracted features of the intermediate convolutional layer, and performing preprocessing on the preprocessed intermediate convolutional layer The features of the stacked layers are encoded to form the middle-level semantic features of the image representation, and the extracted features of the fully connected layer are normalized and used as the high-level semantic features of the image representation; the middle-level semantic features of the image representation and the described The high-level semantic features represented by the image are fused; image classification is performed based on the fused features. Through the present invention, the accuracy rate of image content recognition can be effectively improved, and then the accuracy rate of image classification can be improved.
Description
technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to an image classification method and device. Background technique [0002] Image classification technology refers to the use of computer algorithms to determine the category of image content such as objects and scenes. The image feature representation in image classification technology will directly affect the final judgment result. With the development of deep learning, the convolutional neural network model trained with a large amount of data has become the main image classification technology. This technology uses the fully connected layer in the convolutional neural network as the feature representation of the image, and then uses classifiers such as SoftMax for classification. , and achieved better results in practical applications. [0003] However, the image features of the fully connected layer represent the high-level semantic features of the image, m...
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IPC IPC(8): G06K9/62
CPCG06F18/2411G06F18/253
Inventor 乔宇郭胜黄韡林
Owner SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI