Multi-scale dictionary natural scene image classification method based on latent Dirichlet model

Abstract

The invention discloses a multi-scale dictionary scene image classification method based on latent Dirichlet analysis and mainly aims to solve the problems that the manual marking workload is higher and the classification accuracy is lower by adopting a traditional classification method. The multi-scale dictionary scene image classification method based on the latent Dirichlet analysis comprises the implementation steps of respectively establishing a training set and a test set for natural scene image classification; extracting scale invariant features from the training set to generate a multi-scale dictionary; performing dictionary mapping on images by using the multi-scale dictionary, and generating multi-scale sparse representation vectors by using a BOW (bag of words model); generating a latent semantic topic model of the multi-scale sparse representation vectors by using a Gibbs sampling method to obtain latent semantic topic distribution of the images, and further building a natural scene image classification model; classifying the natural scene images by using the classification model. According to the latent Dirichlet analysis-based method for classifying the scene images by using the multi-scale dictionary disclosed by the invention, by adopting multi-scale features and the latent semantic topic model, the feature information of the images is enriched, a large amount of manual marking work is avoided, and the classification accuracy is improved. The multi-scale dictionary scene image classification method based on the latent Dirichlet analysis can be used for object identification and vehicle and robot navigation.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a method for classifying natural scene images, which can be used for target recognition, detection, intelligent vehicle and robot navigation. Background technique [0002] In the past decade, natural scene image classification has become an important research subject in the field of image processing technology. Natural scene image classification has a wide range of applications, such as object recognition and detection and intelligent vehicle or robot navigation. Natural scene images are still a challenging problem due to large intra-class variability, variability in lighting conditions, and scale variability in the images themselves. [0003] The classification methods of natural scene image classification can be roughly divided into two categories: one is the natural scene image classification method based on the low-level semantic model; the other is the natural scen...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to address the shortcomings of the above-mentioned existing methods, and propose a multi-scale dictionary natural scene image classification method based on the latent Dirichlet model, so as to reduce the need for manual marking, enrich the scale information of image features, and improve the classification accuracy rate

Images