3 research outputs found
A fast image retrieval method designed for network big data
In the field of big data applications, image information is widely used. The value density of information utilization in big data is very low, and how to extract useful
information quickly is very important. So we should transform the unstructured image data source into a form that can be analyzed. In this paper, we proposed a fast image retrieval method which designed for big data. First of all, the feature extraction method is necessary and the feature vectors can be
obtained for every image. Then, it is the most important step for us to encode the image feature vectors and make them into
database, which can optimize the feature structure. Finally, the corresponding similarity matching is used to determined the
retrieval results. There are three main contributions for image retrieval in this paper. New feature extraction method, reasonable elements ranking and appropriate distance metric can improve the algorithm performance. Experiments show that our method
has a great improvement in the effective performance of feature extraction and can also get better search matching results
A Fast Image Retrieval Method Designed for Network Big Data
Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other worksIn the field of big data applications, image information is widely used. The value density of information utilization in big data is very low, and how to extract useful
information quickly is very important. So we should transform the unstructured image data source into a form that can be analyzed. In this paper, we proposed a fast image retrieval method which designed for big data. First of all, the feature extraction method is necessary and the feature vectors can be
obtained for every image. Then, it is the most important step for us to encode the image feature vectors and make them into
database, which can optimize the feature structure. Finally, the corresponding similarity matching is used to determined the
retrieval results. There are three main contributions for image retrieval in this paper. New feature extraction method, reasonable elements ranking and appropriate distance metric can improve the algorithm performance. Experiments show that our method
has a great improvement in the effective performance of feature extraction and can also get better search matching results