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

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...

AI Technical Summary

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

Images