The search based on the text query is to be performed firstly in image re-ranking. Then it returned list of images is according to the visual features similarity reordered. The proposed retrieving method in this paper uses the fusion of the images multimodal information i. e. textual and visual which is a current trend in image retrieval researches. For this association rule mining techniques is used. To improve the retrieval accuracy of content-based image retrieval systems, in proposed system has tries to focus on designing sophisticated low-level feature extraction algorithms to narrowing the 'semantic gap' between the visual features and the richness of human semantics. Proposed worked trying to increased the image retrieval performance by fusing i. e. textual and visual features for retrieving and to reduced the semantic gap problem . The obtained results show that the proposed method achieved the best precision score among different query categories. A Multimodal Fusion method based on Association Rules mining (MFAR) is presented in this paper. It is assume as a late fusion and it uses two different data mining techniques for retrieving images: clustering and association rules mining (ARM) algorithm. In this method there are two main phases: offline and online phase. In the offline phase, The Semantic Association Rules is used to identified the relations among the clusters different modalities in the offline phase. At online phase (retrieving phase) uses the generated ARM, to retrieve the related images of search query.

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Multimodal Fusion Clustering Arm Algorithm.