Representative image selection method based on multi-example active learning

Abstract

The invention relates to the field of machine learning, and specifically relates to a representative image selection method based on multi-example active learning, which comprises the following stepsof: (1) extracting original features of an image; (2) dimension reduction of original features; (3) carrying out original sample image pre-clustering by utilizing the dimensionality reduction characteristics; (4) selecting an initial training sample; (5) training a classifier; (6) adjusting the difficult-to-classify sample set; (7) adjusting the original sample set; (8) repeatedly executing the steps (5) to (7) to carry out iterative training until convergence; and (9) outputting a representative image. Screening out a sample set which contributes to the maximum classification precision of theclassifier from the original samples through pre-clustering, multi-example learning and active learning methods; and the samples are labeled for other machine learning tasks, so that the manpower consumed by labeling can be reduced, a part of noise samples can be filtered out, and the effective operation of other machine learning tasks is ensured.

Description

technical field [0001] The invention relates to the field of machine learning, in particular to a representative image selection method based on multi-instance active learning. Background technique [0002] With the rapid development of Internet technology, with the help of various Internet tools, people can quickly obtain a large amount of data from the Internet. However, the data obtained from the Internet is usually accompanied by more noise and a large amount of content redundancy. In the training process of the machine learning algorithm, if the original data is not cleaned and directly marked, it will not only cause a lot of waste of human resources, but also the algorithm is difficult to achieve the desired training effect due to the influence of noise. Taking advantage of the powerful analytical capabilities of weakly supervised learning, the acquired original data set can be screened first. [0003] According to the degree of training data labeling, machine learnin...

AI Technical Summary

Problems solved by technology

The representative image selection method in the currently published patents mainly focuses on the optimization of the clustering method, and some methods still require manual participation in labeling

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