Multi-labeled scene classification method based on incremental linear discriminant analysis

Abstract

The invention discloses a multi-labeled scene classification method based on incremental linear discriminant analysis and mainly solves the problems of the prior art in image dimensional reduction and multi-label classification. The method includes: decomposing a multi-label into a plurality of single labels; using an initial sample to compute a transformation matrix for initial linear discriminant analysis, using an added sample to incrementally updating the transformation matrix for initial linear discriminant analysis, and projecting high-dimensionality data to a low-dimensionality space; randomly selecting training samples and test samples from a sample set subjected to dimensional reduction, and using a single-sample multi-labeled K-nearest neighbor classifier to classify feature samples subjected to dimensional reduction so as to obtain test sample output values; predicting labels of the test samples to obtain classification results. The method has the advantages that classification precision is higher, classification time is shorter and the multi-labeled data of high quantity, high dimensionality and high diversity can be quickly and accurately classified.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a multi-class image scene classification method, which can be used to quickly and accurately process complex scene detection with rich features and a large number of categories. Background technique [0002] In recent years, as the capacity of computers and other data collection equipment has increased, the size of the data has become larger and larger. A large amount of data emerges due to high dimensionality and data augmentation. It is widely expected that time and resource consumption can be reduced by reducing the dimensionality of the data. Dimensionality reduction can greatly reduce the amount of data, and facilitate subsequent processing by mapping data from a high-dimensional feature space to a low-dimensional feature space. Projections preserve as much information as possible in high-dimensional spaces. A widely used supervised dimensionality red...

AI Technical Summary

Problems solved by technology

However, the incremental linear discriminant analysis algorithm proposed by T.-K.Kim et al. and the GSVD-ILDA algorithm proposed by H.Zhao et al. encountered the same problem, that is, it is difficult to determine the trade-off level between efficiency and performance.

If you remove too many minor components, the performance will deteriorate, otherwise it will be very inefficient

In addition, the performance is very sensitive to parameter settings, and it is not easy to adjust the parameters, resulting in unstable classification results

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