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Relevance is one of the concepts that many researchers consider it fundamental in information retrieval. Recently, relevance has also been given attention in Iran about which multiple studies have been conducted. However, there is no clear understanding of relevance-related studies in Iran. Therefore, the current research aimed at exploring related research through a systematic review. To find the publications, questions and inclusion/exclusion criteria were initially determined. Several databases were searched using related keywords. Finally, 108 publications were selected. The software Excel was used to extract and analyze the data gathered. Publications in the field of relevance could be classified into 6 groups including recall, precision, recall and precision, relevance feedback, relevance judgment, and review resources. The results showed that most of the studies are conducted in an objective way and the researchers considered search engines more than other information retrieval systems. Moreover, the results also indicate that the recent studies are related to relevance judgment and experts in Knowledge and Information Science have been unwilling to do research on relevance feedback. While the quantitative methodology is used more frequently, the qualitative approach could also be very effective, especially in the subjective relevance and user-friendly context.

The purpose of this study is to design a content-based medical image retrieval system and provide a new method to reduce semantic gap between visual features and semantic concepts. Generally performance of the retrieval systems based on only visual contents decrease because these features often fail to describe the high level semantic concepts in user’s mind. In this paper this problem is solved using a new approach based on projection of relevant and irrelevant images in to a new space with low dimensionality and less overlapping in relevance feedback level. For this purpose, first we change the feature space using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) techniques and then classify the feedback images applying Support Vector Machine (SVM) classifier. The proposed framework has been evaluated on a database consisting of 10,000 medical X-ray images of 57 semantic classes. The obtained results show that the proposed approach significantly improves the accuracy of retrieval system.

Relevance feedback approaches is used to improve the performance of content-based image retrieval systems. In this paper, a novel relevance feedback approach based on similarity measure modification in an X-ray image retrieval system based on fuzzy representation using fuzzy attributed relational graph (FARG) is presented. In this approach, optimum weight of each feature in feature vector is calculated using similarity rate between query image and relevant and irrelevant images in user feedback. The calculated weight is used to tune fuzzy graph matching algorithm as a modifier parameter in similarity measure. The standard deviation of the retrieved image features is applied to calculate the optimum weight. The proposed image retrieval system uses a FARG for representation of images, a fuzzy matching graph algorithm as similarity measure and a semantic classifier based on merging scheme for determination of the search space in image database. To evaluate relevance feedback approach in the proposed system, a standard X-ray image database consisting of 10000 images in 57 classes is used. The improvement of the evaluation parameters shows proficiency and efficiency of the proposed system.