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Data balancing strategy and multi-feature fusion-based image labeling method

A multi-feature fusion and image annotation technology, which is applied in the fields of electrical digital data processing, special data processing applications, character and pattern recognition, etc., can solve problems such as weak expressive ability of a single feature feature and unbalanced training image set

Active Publication Date: 2018-09-28
FUJIAN UNIV OF TECH
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Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide a data equalization strategy and a multi-feature fusion image labeling method to overcome the defects in the prior art, and to solve the problem of unbalanced training image sets and weak expression ability of a single feature The problem

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Embodiment Construction

[0036] In order to make the present invention more comprehensible, a preferred embodiment is now described in detail with accompanying drawings as follows.

[0037] like figure 1 As shown, a data balancing strategy and a multi-feature fusion image labeling method of the present invention, the method first adopts the data balancing strategy to expand the semantic group of image training, and input it into the trained deep convolutional neural network , the image is abstracted into a deep feature vector through multiple iterations of convolution and downsampling, and the features of each semantic group image are obtained; then the multi-scale fusion features of each semantic group are calculated; and it is combined with the deep convolutional neural network Multi-feature fusion is performed on the deep features calculated by the network; a complete feature representation of the semantic group is obtained. Use the same method to extract the features of the image to be labeled an...

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Abstract

The invention provides a data balancing strategy and multi-feature fusion-based image labeling method. The method comprises the following steps of: 1, carrying out semantic grouping on training imagesto obtain semantic groups; 2, extending the semantic groups by adoption of a data balancing strategy; 3, inputting the training images into a trained deep convolutional neural network to obtain a deep feature of each image in each semantic group; 4, calculating multi-scale fusion feature of each image in each semantic group; 5, carrying out multi-feature fusion on the multi-scale fusion feature and the deep feature so as to obtain a fused feature of each image in each semantic group; 6, extracting a shallow feature and a deep feature of a to-be-tested image and carrying out feature fusion toobtain a fused feature of the to-be-tested image; and 7, calculating a visual similarity between the fused feature of the to-be-tested image and the fused feature of each image in each semantic group,and sorting the visual similarities to obtain an image labeling result and then obtain a category label. According to the method, the problems that training set images are unbalanced and the single feature expression ability is not strong are solved.

Description

technical field [0001] The invention relates to the fields of pattern recognition and computer vision, in particular to a data equalization strategy and an image labeling method for multi-feature fusion. Background technique [0002] With the continuous development of information science and technology in the field of computer networks and multimedia, electronic imaging products such as various high-definition cameras, digital cameras, and video cameras have been widely used and popularized, which greatly enriches people's work, life and study. Faced with such a large amount of image information, how to efficiently organize and manage these images so that people can quickly and accurately obtain the required information from the massive image information is a very difficult problem in today's world. Image automatic annotation technology has become an important research direction in the field of pattern recognition in recent years because of its wide application scenarios. A...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06K9/62
CPCG06F18/24G06F18/25
Inventor 梁泉张毓峰田健
Owner FUJIAN UNIV OF TECH
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