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Imbalance-learning-based depth convolution network image marking method and apparatus

A deep convolution, network image technology, applied in the field of image annotation, can solve problems such as difficult image annotation

Active Publication Date: 2016-06-01
INST OF AUTOMATION CHINESE ACAD OF SCI
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Problems solved by technology

However, how to design more powerful image features and take into account the non-uniform distribution of image category labels has always been a difficult point in the field of image annotation.

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Embodiment Construction

[0030] In order to make the purpose, technical solutions and advantages of the present invention clearer, the following in conjunction with specific examples, and with reference to the appended figure 1 , to further describe the present invention in detail.

[0031] The present invention proposes a deep convolutional network image labeling method and device based on unbalanced learning.

[0032] First, the present invention constructs a deep convolutional network to extract the depth features of the image. A deep convolutional network mainly consists of three components: a convolutional layer, a downsampling layer, and a fully connected layer. The convolutional layer deconvolutes the input image with a trainable convolution kernel (the first stage is the input image, and the subsequent stage is the feature map), and then adds a bias to obtain the convolutional layer. The neuron weights of the convolutional layer on the same feature map surface are the same, which reduces the...

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Abstract

The invention discloses an imbalance-learning-based depth convolution network image marking method and apparatus. The method comprises: step one, pretreatment is carried out on a to-be-identified image to obtain an original pixel of the to-be-identified image; step two, the original pixel of the to-be-identified image is inputted into a depth convolution neural network to obtain depth feature representation information of the to-be-identified image; and step three, marking information of the to-be-identified image is predicted by using the depth feature representation information. According to the method provided by the invention, distribution of the image dictionary vocabulary is taken into consideration and layer-by-layer extraction is carried out on the original pixel of the image by the depth convolution network. Compared with the traditional marking method, the provided marking method has high precision.

Description

technical field [0001] The invention relates to the technical field of image labeling, in particular to a deep convolutional network image labeling method based on unbalanced learning. Background technique [0002] In the era of big data, the information resources that people can access are showing explosive growth, and massive images and video information are born on the Internet every day. In order to effectively organize, query and browse such a large-scale image resource, image retrieval technology emerges as the times require. Text-Based Image Retrieval (TBIR) is an important image retrieval method. For TBIR, it requires users to submit text as a query, therefore, image retrieval needs to obtain the textual semantic information of images in advance. Image tagging is an effective method to obtain semantic information of images, and it is widely used, for example, in social media, a large number of images are tagged by users. However, there are a large number of unlabe...

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Application Information

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IPC IPC(8): G06K9/62G06N3/08
CPCG06N3/084G06F18/214
Inventor 张文生杨阳
Owner INST OF AUTOMATION CHINESE ACAD OF SCI