Research On Automatic Image Annotation Algorithm Based On Sparse Representation

With the advent of the era of large data,computer technology innovation and network information exchange day,our production lifestyle changes every day.There have been a variety of innovative ways of generating information and an unprecedented access to the way.Massive picture,image data is not only large base and the speed of update change is even more amazing.How to mark the image,annotation of the image more quickly and more accurately become a hot research.Most of the past image annotations are manually annotated,with low efficiency,low accuracy and long time consuming.Consume a lot of manpower and material resources,the result is not ideal.The choice of marked words will be subjective.As the mass of image data increases,people need to automate the image annotation algorithm.Automatic image annotation also laid the foundation for the post-image query,collation,storage,classification and other work.In order to improve the efficiency of image automatic labeling,overcome the semantic gap between textual information and visual features in image annotation,obtaining a text key that accurately describes the image information.This paper presents an image automatic labeling algorithm based on sparse expression,focuses on the feature selection and thinning of images,and the experiment is carried out to verify the effectiveness of the method.The main contents are as follows:1.On the basis of sparse modeling,reduce the image dimension to solve the image data redundancyOn the basis of establishing sparse model,combined with the coefficients of wavelet transform,the advantages of different image features in visual perception are analyzed,the visualization of image data is quantified,the image data is expressed as vector form,The weight of the eigenvector,reduce the redundant vector of different images,and realize the completeness and accuracy of the image feature extraction.2.We present the feature vector of the multi-feature fusion to solve the problem of sparse expression of the corresponding imageIn this paper,the image feature extraction work,combined with a variety of underlying features to describe the image of the relevant information,and strive to achieve a comprehensive and accurate.By extracting the SIFT feature and HSV feature of the image,the sparse model is established,and the sparse coefficient is obtained by using the distance function to measure the sparse coefficient of the eigenvector.The similarity of the image content is realized,So the measured image is automatically marked.3.A method of selecting the image tag words based on the feature coefficient matrix is proposedIn this paper,based on the sparse expression of the image annotation,to achieve the SIFT feature and color characteristics of the weighted fusion,the full use of the image data sparse and from the known information to be marked image mapping,build sparse coefficient matrix,And the similarity coefficients of the image sub-blocks in the dictionary are sorted,the markings of the partial images are determined,and the complete image is selected to select the marked words,the automatic marking of the images is completed,the quality of the image feature extraction is improved,the experimental data for the image processing is simplified,Reducing computational time complexity.