Review: Data Mining Approach for Image Retrieval in Multimodal Fusion Using Frequent Pattern Tree


Author(s): P.B.Kamdi; Prof. Pravin Kullurkar

The retrieving method proposed in this project utilizes the fusion of the images multimodal information (textual and visual) which is a recent trend in image retrieval researches. Retrieving method combines two different data mining techniques to retrieve semantically related images: clustering and Frequent Pattern Tree Based Algorithm. The clustering technique is constructed at the offline phase. The frequent pattern rules are used between the text semantic clusters and the visual clusters of the images to use it at the online phase. Efficient algorithms for mining frequent item sets are crucial for mining association rules as well as for many other data mining tasks. Methods for mining frequent item sets have been implemented using an FP-tree, for storing compressed information about frequent item sets. In this project image with text as an input and produce accurate occurrences of the image, Lot of experimental results have demonstrated that these algorithms perform extremely well.