Image classification method and system based on hierarchical multi-task learning

Abstract

The invention discloses an image classification method and system based on hierarchical multi-task learning. The method comprises the following steps of: firstly, carrying out hierarchical category labeling on images in a training sample set from coarse to fine by utilizing a relation between image categories through experience knowledge of experts in some professional fields; secondly, constructing a hierarchical deep convolutional neural network comprising a feature extraction module, a coarse classification layer and a fine classification layer; secondly, training the coarse classificationlayer and the fine classification layer by adopting a multi-task learning algorithm; and finally, classifying the input test images by using the trained hierarchical deep convolutional neural network.The hierarchical structure information between the image categories is combined with the convolutional neural network, the hierarchical deep convolutional neural network is designed, and a multi-tasklearning algorithm is utilized to realize coarse-to-fine classification of the images.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an image classification method and system based on hierarchical multi-task learning. Background technique [0002] Image classification is a fundamental problem in the field of pattern recognition. Image classification is a method of dividing images into different categories according to the characteristics of the image itself. Image classification in pattern recognition mainly refers to the process of using computer to replace human visual judgment and understanding, and automatically classifying image categories through some specific algorithms. [0003] In image datasets, there may be a problem that the visual separability of image classes is not uniform, that is, some classes are more difficult to distinguish than others. For example, it is easy to distinguish between apples and buses, but it is difficult to distinguish apples from oranges. In fact, both apples and oranges ...

AI Technical Summary

Problems solved by technology

However, when dealing with image classification tasks, most classification algorithms usually process each image category separately, thus ignoring the hierarchical structure between image categories

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