Fine-grained image classification method based on depth convolution neural network

A deep convolution and neural network technology, applied in the field of fine-grained image classification based on deep convolutional neural network, can solve problems such as slow convergence speed, high computational complexity, and dependence on accurate component detection

Active Publication Date: 2018-12-14
XI AN JIAOTONG UNIV
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Problems solved by technology

[0003] The part-based algorithm fine-grained image classification method first detects different parts of the target object, and then increases the difference between classes and reduces the difference within the class through local feature modeling. This type of method i

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  • Fine-grained image classification method based on depth convolution neural network
  • Fine-grained image classification method based on depth convolution neural network
  • Fine-grained image classification method based on depth convolution neural network

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Embodiment Construction

[0063] In view of the main challenges of fine-grained image classification (similarity between classes and diversity within classes), this invention proposes to improve the performance of deep convolutional neural networks by using class label hierarchical structure relations, cascading softmax loss and generalization large-margin loss. Fine-grained image classification performance. Specifically, the present invention improves the fine-grained image classification accuracy of the deep convolutional neural network from the following two aspects. First, for a given deep convolutional neural network, in order to better utilize the h-level hierarchical structure relationship between fine-grained class labels, the present invention proposes to use h fully connected layers to replace the last fully connected neural network Layers, the parameters of these new fully connected layers are learned with the cascaded softmax loss proposed by the present invention. Second, the present inve...

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Abstract

The invention discloses a fine-grained image classification method based on a depth convolution neural network, comprising the following steps: 1) preparing a fine-grained image classification data set, and dividing the training data into a training data set and a verification data set; 2) building a depth convolution neural network model for fine-grained image classification, training the model with a training data set, and saving the network model parameters when the trained model reaches the set precision on the verification data set; 3) calculating the tags of test image classification orclassification accuracy of test data sets by using the trained model. The fine-grained image classification framework provided by the invention is independent of and can be applied to any DCNN structure, and has good portability.

Description

technical field [0001] The invention belongs to the technical field of computer vision image classification, and in particular relates to a fine-grained image classification method based on a deep convolutional neural network. Background technique [0002] The difference between the fine-grained image classification task and the general image classification task is that the granularity of the category to which the image belongs is finer, and the difference between different fine-grained object classes is only reflected in the subtleties. The main challenge of fine-grained image classification lies in the inter-class similarity and intra-class diversity. On the one hand, the visual differences between different fine-grained classes are only reflected in subtleties; on the other hand, due to the influence of location, viewing angle, lighting and other conditions, even instances of the same class may have large changes. Intra-class visual differences. For example, the differe...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06N3/045G06F18/2414G06F18/214
Inventor 张玥龚怡宏石伟伟程德陶小语
Owner XI AN JIAOTONG UNIV
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