Interactive classification labeling method for sketch data set

Abstract

The invention discloses an interactive classification labeling method for a sketch data set in a computer. The interactive classification labeling method comprises the following steps: in a learning process, carrying out multi-feature extraction on a labeled sketch data set, carrying out metric learning of feature space, and calculating a distance measurement function; in a selection process, if judging that non-labeled sketches do not exist in the sketch data set, coming to an end to obtain a final result; or, according to the result of metric learning, carrying out feature space construction on the sketch data set to be labeled, carrying out layering clustering, and selecting an optimal sample subset; carrying out online labeling, carrying out interactive confirmation on the sketches in the optimal sample subset, carrying out classification labeling on a confirmed sample, and updating the labeled sketch data set; maintaining the non-labeling state of remaining non-similar sketches, and updating the sketch data set to be labeled; then constantly circulating the process until the user completes all sketch labeling to obtain a final labeling result.

Description

technical field [0001] The invention relates to a processing method of a visual data set, which belongs to the technical field of computer vision, in particular to an interactive category labeling method for a sketch data set in a computer. Background technique [0002] As one of the oldest communication methods of human beings, sketching is the basic way for human beings to abstract and conceptualize visual information. In recent years, with the popularization of touch interactive devices such as smartphones and tablets, sketching has become one of the most common ways of human-computer interaction, and has been used in image and model retrieval, such as literature 1: M.Eitz, K.Hildebrand, T. Boubekeur and M. Alexa. Sketch-based image retrieval: Benchmark and bag-of-features descriptors. IEEE Transactions on Visualization and Computer Graphics, vol.17, no.11, pp.1624–1636, 2011., Literature 2 : M.Eitz, R.Richter, T.Boubekeur, K.Hildebrand and M.Alexa. Sketch-based shape re...

AI Technical Summary

Problems solved by technology

[0005] To sum up, when labeling the sketch dataset as a whole, the existing supervised sketch classification methods need to predict the category and sample training, which cannot be applied to the situation where the label category is unknown or uncertain; while the unsupervised image category discovery method There will be problems in the following three aspects: 1) the method of "labeling all at once" is adopted, which relies on the classification effect of unsupervised learning, and it is difficult to guarantee the correctness of the labeling results; 2) only the Euclidean distance calculation between the underlying features of the sample is used Similarity, ignoring the category information provided by the user in the labeling process, it is difficult to fully reflect the user's interaction intention; 3) When providing category labels for the clusters to be labeled, there is a lack of reasonable screening strategies to effectively reduce the labeling burden

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