The Research Of Scene And Place Recognition Using An Improvement LDA Topic Model

As one of the directions in the field of computer vision research image classification is the basis of other image application fields.An important solution to this problem is scene image classification.In this paper,we propose a new and improved method to improve the classification accuracy and efficiency of the scene model by using the LDA topic model based on the existing problems in the scene classification technology of LDA topic model.The Latent Dirichlet Allocation(LDA)topic model is a widely used image processing method,which abstracts the underlying features of the image into visual words,generates visual dictionaries,counts the frequency of visual words and then establishes the middle semantic representation model that represents an image.After the classifier automatically marks the image scene tag,to achieve automatic classification of the image.In view of the traditional model in the image scene recognition problems,this paper carried out the following research:1.The K-Means++ clustering algorithm is used to generate the visual word for the problem that the traditional model is less efficient in the method of image scene recognition.2.The traditional method of word bag means that the image does not consider the weight of the word,so that the subject of the distribution of the tendency of high frequency words,for the visual word appears power law distribution problem,this paper uses the weighted statistical histogram for image representation.3.The method of improving the image scene recognition model can be used to enhance the function of the important features in the classification and recognition,and the potential Dirichlet distribution of the eigenfunctions is proposed by introducing the feature function into the image scene recognition.Featured Latent Dirichlet Allocation(FLDA)theme model to improve the classification and recognition of scene images.4.The parameters in the LDA model are difficult to estimate directly.For the FLDA model,an improved variational inference method,Fast Variational Inference(FVI),is proposed to reduce the number of iterations and reduce the number of parameters in the model Cost,improve the efficiency of the implementation of the model.By analyzing the results of multiple experiments on different data sets,the FLDA model and the fast variational inference algorithm proposed in this paper can effectively improve the accuracy and efficiency of the image scene classification based on the theme model,and have some general Sexuality and stability.