Fuzzy Relevance Feedback in the Semantic Image Retrieval

In this paper, a new system of fuzzy relevance feedback for image retrieval is introduced. In conventional CBIR systems, the users are restricted to make a binary labeling on the retrieval results, while this determination is difficult for rich images in semantic. In view of this, a fuzzy relevance feedback approach which enables the user to make a fuzzy judgment is proposed. In the proposed system, namely Semantic Image Retrieval based on Fuzzy Transaction Repository, we accumulate user interactions using a soft feedback model to construct Fuzzy Transaction Repository (FTR). The repository remembers the user's intent and, therefore, in terms of the semantic meanings, provides a better representation of each image in the database. To best exploit the benefits of user feedback, we improved the proposed system, so that the repository remembers the user's intent in a suitable manner (as structure-based Fuzzy Transaction Repository) and provides an accurate representation for each image in the database. The semantic similarity between the query and each database image can then be computed using the current feedback and the semantic values in the structure-based FTR. Furthermore, feature re-weighting is applied to the session-term feedback in order to learn the weight of low-level features. These two similarity measures are normalized and combined together to form the overall similarity measure. Our experimental results show that the average precision of the proposed systems exceeds 83% after three iterations. Furthermore, in the long term, the average retrieval accuracy of the improved system surpasses the first.