Joint graph regularization based semantic analysis for cross-media retrieval: A systematic review

The large number of heterogeneous data are rapidly increasing in the internet and most data consist of audio, video, text and images. The searching of the required data from the large database is difficult and time taking process. The single media retrieval is used to get the needed data from the large dataset and it has the drawback, it can only retrieve the single media only. If the query is given as the text and acquired result are present in text. The users demand the cross-media retrieval for their queries and it is very consistent in providing the result. This helps the users to get more information regarding to their queries. Finding the similarities between the heterogeneous data is very complex. Many research is done on the cross-media retrieval with different methods and provide the different result. The aim is to analysis the different cross-media retrieval with the joint graph regularization (JGR) to understand the various technique. The most of researches are using the parameter of MAP, precision and recall for their research. © 2018 Authors.